CN112749321B

CN112749321B - Data processing method, client, server, system and storage medium

Info

Publication number: CN112749321B
Application number: CN201911051343.XA
Authority: CN
Inventors: 张明耀
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2019-10-31
Filing date: 2019-10-31
Publication date: 2024-05-28
Anticipated expiration: 2039-10-31
Also published as: CN112749321A

Abstract

The embodiment of the application provides a data processing method, a client, a server, a system and a storage medium. In the data processing system, based on interaction between the client and the server, when a first user initiates an operation of checking the health data of a second user, query intention of the first user can be estimated, target health data matched with the query intention of the first user in the health data of the second user is displayed, intelligent recommendation of the health data is further realized, the health data meeting query requirements of the first user can be displayed to the first user quickly, and data query efficiency is improved.

Description

Data processing method, client, server, system and storage medium

Technical Field

The present application relates to the field of health informatization technologies, and in particular, to a data processing method, a client, a server, a system, and a storage medium.

Background

For doctors, when diagnosing the disease condition of patients, the historic disease condition and the historic diagnosis and treatment mode of the patients are often used as the reference basis for the current diagnosis. However, for most patients, the previous medical conditions and diagnosis and treatment modes of the patients cannot be memorized or described effectively when the patients go to medical treatment. Therefore, a new solution is to be proposed.

Disclosure of Invention

The aspects of the application provide a data processing method, a client, a server, a system and a storage medium, so that intelligent recommendation of health data is realized, the health data meeting the query requirement of a first user can be quickly displayed, and the data query efficiency is improved.

The embodiment of the application provides a data processing method, which is suitable for a client and comprises the following steps: responding to the operation of inquiring the health data of the second user by the first user, and sending a corresponding data acquisition request to a server; receiving target health data of the second user returned by the server according to the data acquisition request; and displaying the target health data, wherein the target health data is matched with the query intention of the first user estimated by the server.

The embodiment of the application provides a data processing method, which is suitable for a server and comprises the following steps: receiving a data acquisition request, wherein the data acquisition request is sent by a client when a first user inquires health data of a second user; estimating the query intention of the first user according to the data acquisition request; acquiring health data matched with the query intention of the first user from the health data corresponding to the second user as target health data; and sending the target health data to the client for display.

The embodiment of the application provides a health data display device, which comprises: the request module is used for responding to the operation of inquiring the health data of the second user by the first user and sending a corresponding data acquisition request to the server; the receiving module is used for receiving target health data of the second user returned by the server according to the data acquisition request; the display module is used for displaying the target health data, and the target health data is matched with the query intention of the first user estimated by the server.

The embodiment of the application provides a health data display device, which comprises: the receiving module is used for receiving a data acquisition request, and the data acquisition request is sent by the client when the first user inquires the health data of the second user; the intention estimating module is used for estimating the query intention of the first user according to the data acquisition request; the data acquisition module is used for acquiring health data matched with the query intention of the first user from the health data corresponding to the second user as target health data; and the sending module is used for sending the target health data to the client for display.

The embodiment of the application provides a client, which comprises the following steps: a memory, a processor, and a communication component; the memory is used for storing one or more computer instructions; the processor is configured to execute the one or more computer instructions to: the steps in the data processing method provided by the embodiment of the application are executed.

The embodiment of the application provides a server, which comprises: a memory, a processor, and a communication component; the memory is used for storing one or more computer instructions; the processor is configured to execute the one or more computer instructions to: the steps in the data processing method provided by the embodiment of the application are executed.

The embodiment of the application provides a computer readable storage medium storing a computer program, which when executed can implement the steps in the data processing method provided by the embodiment of the application.

The embodiment of the application also provides a data processing system, which comprises: a client and a server; wherein, the client is used for: according to the operation of inquiring the health data of the second user by the first user, a corresponding data acquisition request is sent to the server, target health data of the second user returned by the server is received, and the target health data is displayed; the server is used for: when the data acquisition request is received, estimating the query intention of the first user, acquiring health data matched with the query intention of the first user from the health data corresponding to the second user, taking the health data as target health data, and sending the target health data to the client.

According to the data processing system provided by the embodiment of the application, based on interaction between the client and the server, when the first user initiates the operation of checking the health data of the second user, the query intention of the first user can be estimated, and the target health data matched with the query intention of the first user in the health data of the second user is displayed, so that intelligent recommendation of the health data is realized, the health data meeting the query requirement of the first user can be conveniently and quickly displayed to the first user, and the data query efficiency is improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1a is a schematic diagram of a data processing system according to an exemplary embodiment of the present application;

FIG. 1b is a schematic diagram of acquiring a code vector of a department according to an exemplary embodiment of the present application;

FIG. 1c is a schematic diagram of acquiring target health data according to a code vector of a department according to an exemplary embodiment of the present application;

FIG. 2a is a schematic illustration of a client page provided in accordance with an exemplary embodiment of the present application;

FIG. 2b is a schematic illustration of a client page provided in accordance with another exemplary embodiment of the present application;

FIG. 3a is a schematic illustration of a detail data presentation page provided in accordance with an exemplary embodiment of the present application;

FIG. 3b is a schematic illustration of a detail data presentation page provided in accordance with another exemplary embodiment of the present application;

FIG. 3c is a schematic illustration of a detail data presentation page provided in accordance with yet another exemplary embodiment of the present application;

FIG. 4 is a flow chart of a data processing method according to an exemplary embodiment of the present application;

FIG. 5 is a flow chart of a data processing method according to another exemplary embodiment of the present application;

fig. 6 is a schematic structural diagram of a health data display device according to an exemplary embodiment of the present application;

Fig. 7 is a schematic structural diagram of a health data display device according to another exemplary embodiment of the present application;

fig. 8 is a schematic structural diagram of a client according to an exemplary embodiment of the present application;

fig. 9 is a schematic structural diagram of a server according to an exemplary embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Aiming at the defect that in the prior art, when a patient goes to medical treatment before, the patient cannot accurately provide own past illness state and diagnosis and treatment mode to provide reference basis for the present medical treatment, in some embodiments of the present application, a solution is provided, and in the following, the technical solutions provided by the embodiments of the present application will be described in detail with reference to the accompanying drawings.

FIG. 1a is a schematic diagram of a data processing system according to an exemplary embodiment of the present application, and as shown in FIG. 1a, the data processing system 100 includes: a client 10 and a server 20.

In the present embodiment, the client 10 refers to a device that is capable of providing a health data query operation to a first user, and has a communication function. The implementation of the client 10 may also vary in different application scenarios. For example, in some scenarios, the client 10 may appear as a user-side cell phone, tablet, computer device, or the like. The first user may initiate a query operation for health data through a plug-in, an application, or a browser or the like provided by the above-mentioned client 10.

In some embodiments, the client 10 may include a touch screen through which the first user may initiate a query operation for health data. Of course, in other embodiments, the client 10 may include a physical key, an external keyboard, a mouse, or a voice input device, etc. that facilitate the first user to initiate the health data query operation, which is not described herein.

The server 20 refers to a device capable of providing a health data management function, a health data access function, a health data analysis calculation function, and a communication function. In some embodiments, the server 20 may be implemented as a conventional server, a cloud host, a virtual center, or the like, which is not limited in this embodiment. The server device mainly includes a processor, a hard disk, a memory, a system bus, and the like, which are similar to a general computer architecture and will not be described again.

In the data processing system 100, the client 10 may send a corresponding data acquisition request to the server 20 according to an operation of the first user to query the health data of the second user, so as to request the server 20 to provide the health data of the second user. When the server 20 receives the data acquisition request sent by the client 10, the query intention of the first user can be estimated; next, from the health data corresponding to the second user, the health data adapted to the query intention of the first user is acquired as target health data, and the target health data is transmitted to the client 10. When the client 10 receives the target health data of the second user returned by the server 20, the target health data may be displayed for the first user to view.

The health data may include medical data such as cases, medical records, etc. generated by the second user when the second user makes a doctor at the medical institution, or may include a physical examination report of the second user when the second user makes a physical examination at the medical institution or the physical examination institution, and may further include other data affecting the health condition, such as living environment, working environment, eating habits, sleep data, exercise data, etc., of the second user, which is not limited in this embodiment.

Wherein, medical data such as case, case history, medical records that the second user produced when medical institution is at the time of seeing a doctor includes: medical data generated by a second user when medical institutions belonging to different regions perform diagnosis and treatment; and/or medical data generated by the second user when a department affiliated with a different medical institution makes a diagnosis. That is, medical data generated by different hospitals of a second user at different places can be shared, and medical data generated by different departments can also be shared. Furthermore, the coverage scope of the health data of the second user can be greatly enriched, the health data of the second user can be summarized comprehensively, consultation of different doctors aiming at certain symptoms of the user is facilitated, and repeated description is omitted.

The query intention of the first user is used for screening partial data from a large amount of health data so as to meet the query requirement of the first user. The part of the data screened out may include health data of interest of the first user, or health data meeting actual business requirements of the first user, which is not limited in the embodiment of the present application.

In this embodiment, when the server 20 receives the data acquisition request sent by the client 10, the query intention of the first user may be estimated. It should be understood that estimating the query intent of the first user refers to estimating the query intent in advance when the first user does not provide the query intent, and further, the data that may satisfy the query requirement of the first user may be actively recommended to the first user without the first user being aware.

The server 20 may be implemented in conjunction with at least one intent description data when predicting the first user's query intent. Wherein the intent description data may include any data capable of reflecting the query intent of the first user, and the present embodiment is not limited.

In some embodiments, the at least one intent description data may include: first intent description data associated with a first user and/or second intent description data associated with a second user. The implementation content of the first intention description data and the second intention description data is different in different application scenarios, which is not limited in this embodiment.

In the data processing system 100, in order to implement the above-mentioned interaction procedure between the client 10 and the server 20, the client 10 and the server 20 may establish a communication connection, and a specific communication connection manner may depend on an actual application scenario.

In some exemplary embodiments, the client 10 and the server 20 may communicate using a wired communication method and a wireless communication method. The wireless communication modes include short-distance communication modes such as Bluetooth, zigBee, infrared rays, wiFi (WIreless-Fidelity, wireless Fidelity technology) and the like, long-distance wireless communication modes such as LORA and the like, and wireless communication modes based on a mobile network. When the mobile network is in communication connection, the network system of the mobile network can be any one of 2G (GSM), 2.5G (GPRS), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4g+ (lte+), 5G, wiMax, and the like.

In this embodiment, based on the interaction between the client 10 and the server 20, when the first user initiates the operation of checking the health data of the second user, the query intention of the first user can be estimated, and the target health data matched with the query intention of the first user in the health data of the second user is displayed, so that intelligent recommendation of the health data is realized, the health data meeting the query requirement of the first user can be quickly displayed to the first user, and the data query efficiency is improved.

In the above and below embodiments of the present application, the first user and the second user may be different users or the same user, and the present embodiment is not limited thereto. Under different application scenarios, the first user and the second user may be implemented as different roles.

For example, in some scenarios, a first user may be implemented as a medical staff member of a medical facility, a doctor, a nurse, etc., and a second user may be implemented as a patient for a visit. Doctors can assist patients in disease diagnosis and treatment by querying their health data.

In other scenarios, the first user and the second user may be implemented as the same user, and the user may learn about their health status by querying their health data.

In still other scenarios, the first user may be implemented as a guardian of the second user, who may learn about the health condition of the guardian by querying the health data of the guardian.

In the following embodiments, a data processing system 100 provided in an embodiment of the present application will be further described by taking a first user implemented as a doctor and a second user implemented as a patient.

In the above and below embodiments of the present application, the operation of the server 20 to determine the target health data from the health data according to the query intention may be implemented based on the data tag corresponding to the health data and the data tag included in the query intention. For convenience of description, the data tag corresponding to the health data is marked as a first data tag, and the data tag intended to be included in the query is marked as a second data tag.

Alternatively, the server 20 may perform data mining and analysis on the health data of the second user when managing the health data of the second user, and add the first data tag to the health data according to the result of the data mining and analysis.

In some embodiments, the first data tag may be a category tag obtained by categorizing the health data. For example, data corresponding to different diseases in the health data may be labeled with disease. For example, the medical data generated when the second user visits the cold may be labeled with a cold label, the medical data generated when the second user visits gastroenteritis may be labeled with a gastroenteritis label, and the medical data generated when the second user visits the coronary heart disease may be labeled with a coronary heart disease label. For example, the data corresponding to the diseases belonging to different departments among the health data may be labeled, for example, the medical data generated when the second user makes a diagnosis of skin diseases may be labeled with a dermatological label, and the medical data generated when the second user makes a diagnosis of cataract may be labeled with an ophthalmic label. For another example, each disease label may be labeled with a disease group to which the disease belongs in the medical field. For example, respiratory disease labels may be applied to cold labels, digestive system disease labels may be applied to gastroenteritis labels, and circulatory system disease labels may be applied to coronary heart disease labels, which are not described in detail.

In other embodiments, the first data tag may be a data tag obtained by classifying items of data included in each case. For example, for a case, the data related to the assay may be labeled with the assay, the data related to the operation may be labeled with the operation, and the data related to the type of disease may be labeled with the type of disease, which is not described in detail.

Based on this, when the query intention of the first user is estimated, the server 20 may obtain the second data tag corresponding to the query intention. And then, inquiring at least one data tag matched with the second data tag in the first data tag, and taking the health data corresponding to the at least one data tag as target health data.

For example, if it is estimated that the query intent of the first user is implemented as: the second user's cold data is queried. At this time, the server 20 may consider the data tag corresponding to the query intention as a cold tag or a respiratory disease tag. Server 20 may then obtain, from the second user's health data, a portion of the medical data that is compatible with the cold label or respiratory disease label for display.

In the foregoing embodiment, the technical solution is described that the server 20 may estimate the query intention of the first user in combination with the first intention description data and/or the second intention description data. An embodiment of acquiring the first intention description data and the second intention description data will be exemplarily described below.

In some exemplary embodiments, the identification information of the second user may be input when the first user initiates an operation to query the client 10 for the health data of the second user. Alternatively, when the client 10 sends the data acquisition request to the server 20, the identification of the first user and the identification of the second user may be added to the data acquisition request. The identification mark can be implemented as an identity mark such as an account number, an identity number, a name and the like, and can also be implemented as a biological characteristic mark of a fingerprint, an iris and a facial image, and the embodiment is not limited.

Based on the above, after receiving the data acquisition request, the server 20 may acquire the first intention description data associated with the first user according to the identification identifier of the first user carried by the data acquisition request, and/or acquire the second intention description data associated with the second user according to the identification identifier of the second user carried by the data acquisition request.

In some embodiments, optionally, the first intent description data may include: at least one of attribute information of the first user, historical query behavior, and preset query conditions.

Alternatively, the attribute information may include: at least one of affiliated medical institutions, affiliated departments, good areas, indications, historic diagnosis and treatment records. When the first user is implemented as a doctor, its attribute information may reflect which health data of the patient is needed as an aid when he actually performs the disease diagnosis service. For example, when a doctor is affiliated with maxillofacial surgery in an oral hospital and is adept at the field of orthodontic treatment, the doctor's query intent may be considered to be correlated with the patient's oral health data.

In some exemplary embodiments, the attribute information of the first user is carried by a data acquisition request sent by the client 10. When the server 20 receives the data acquisition request, the data acquisition request may be parsed to acquire attribute information of the first user.

In other exemplary embodiments, the attribute information of the first user and its correspondence to the identification of the first user is maintained at the server 20. After the identification of the first user acquired from the data acquisition request, the server 20 may acquire attribute information of the first user based on the above correspondence.

Optionally, the historical query behavior of the first user may include: at least one of query sequence, query frequency and input query keywords when the first user queries different health data in the historical time period. When the first user is implemented as a doctor, the historical query behavior of the first user can reflect the query habit of the first user and the attention degree of the first user to different health data. Based on the degree of attention, the doctor's current query intent can be predicted.

Optionally, the query condition preset by the first user may be a preset query condition of the first user before querying the health data of the second user, where the query condition may include: at least one of a query time period, a query keyword, a data category and a data label corresponding to the data.

In some embodiments, optionally, the second intent description data may include: at least one of a historical visit department of the second user, a distribution characteristic of the health data of the second user, a time of generation of the different health data, and a frequency of querying the different health data.

Optionally, the historical office of the second user refers to an office that the second user has visited in a set historical time period or all offices that the second user has visited in the past. Each time the second user goes to a department visit, health data corresponding to the department can be generated, such as disease diagnosis results, medication prescriptions, surgical conditions, laboratory conditions, and the like, generated by the visit. The department with the previous diagnosis of the user can reflect the previous medical history of the user, and the previous medical history has a certain influence on the current diagnosis of the user or the department, so that the query intention of the first user can be estimated through the department with the previous diagnosis of the second user.

Alternatively, the distribution characteristics of the health data of the second user may be represented as the data amount of the health data of the second user occupied by the medical data corresponding to different disease types. If the amount of medical data corresponding to one or more disease types is large, the disease type may be considered to have a large impact on the health of the second user. Therefore, the distribution characteristics of the health data of the second user are used as the intention description data, so that the first user is prompted to pay attention to the medical data corresponding to the disease type.

Alternatively, the time of generation of the different health data of the second user may reflect the dynamic change of the health data of the second user. It should be appreciated that the health data of the second user in a time period closer to the current time is more beneficial to assist in analyzing the current health condition of the second user. Therefore, the generation time of the different health data of the second user is used as the intention description data, so that the first user is prompted to pay attention to the health data of the second user in a time period closer to the current moment.

Optionally, the query frequency of the different health data of the second user may reflect which data of the different health data of the second user may make a greater contribution to assisting in analyzing the health condition of the second user. Therefore, the query frequency of different health data is used as the intention description data, so that the first user is prompted to pay attention to the health data which make more contribution.

Of course, the above-listed intent description data that may be used to estimate the first user's query intent is merely for exemplary purposes and is not limiting of the scope of the present application.

Based on the intent description data listed above, different query intents of the first user may be predicted and different target health data obtained, as will be exemplified below.

In an alternative embodiment, the first intent description data may include: the first user affiliated department, and the second intent description data may include a historical visit department of the second user. The estimated query intent of the first user may be: and inquiring the health data corresponding to the first historical visit department of the second user.

The first historical office refers to a historical office adapted to the current office of the second user, and the first office is used for convenience of description. Alternatively, the first historical office may include one historical office or a plurality of historical offices, which is not limited in this embodiment. An embodiment of estimating the first historical visit department will be exemplarily described below with reference to the first intent description data and the second intent description data.

Optionally, when the second user is a doctor, the department to which the doctor who the patient visits belongs is the department to which the patient currently visits. Therefore, the department affiliated to the first user can be obtained as the current visit department of the second user, and the department corresponding to the at least one history visit record of the second user can be obtained as the at least one history visit department of the second user. Then, the similarity between the current visit department and at least one historical visit department is calculated. If, based on the calculated similarity, a historical visit department with a similarity to the current visit department greater than the set similarity threshold is selected from at least one historical visit department, the selected historical visit department may be used as a first historical visit department, and the query intent of the first user is estimated as: health data generated by the second user at the time of the first historical office visit is queried.

In this embodiment, optionally, when calculating the similarity between the current office and at least one historical office, the coding vector corresponding to the current office may be obtained according to the diagnostic codes of the diseases included in the current office and tf-idf (term frequency-inverse text frequency index) corresponding to the diagnostic codes; meanwhile, acquiring respective corresponding coding vectors of at least one historical visit department; and then, respectively calculating the similarity of the coding vector corresponding to the current medical department and the coding vector corresponding to at least one historical medical department so as to obtain the similarity of the current medical department and the at least one historical medical department.

In the following, an embodiment of calculating the coding vector corresponding to a department will be exemplarily described by taking an arbitrary department as an example with reference to fig. 1 b.

Each department can diagnose a plurality of different diseases, and in order to obtain the code vector of the department, the embodiment can obtain the diagnosis codes corresponding to different disease types in the department. Wherein the diagnostic codes corresponding to disease types include, but are not limited to, international unified disease classification (International Classification of Diseases, ICD) codes formulated by world health organization (World Health Organization, abbreviated WHO): ICD-10, ICD-9, or International Primary diagnosis and treat Classification (International Classification of PRIMARY CARE) code: ICPC2. It should be noted that in some typical scenarios, the disease diagnosis may be recorded in the form of ICD-10, ICD-9, ICPC2, or a diagnosis name by different medical institutions and different departments. Therefore, in this embodiment, as shown in fig. 1b, when the code vector corresponding to each department is obtained, the description modes of the disease types in the departments and between the departments can be unified in advance.

Alternatively, in some embodiments, the disease types contained in each department may be collectively described as ICD-10 codes or ICD-9 codes or other types of codes for each department, and the present embodiments are not limited. Taking the unified description as ICD-10 coding as an example, the disease type originally described by ICD-9 coding can be converted into ICD-10 coding according to the corresponding tables of ICD-9 and ICD-10; the disease type originally described by ICPC2 coding can be converted into ICD-10 coding according to the one-to-many relationship between ICPC2 and ICD-10; if the disease type is originally described by a diagnosis name, the text similarity of the diagnosis name and the standard diagnosis name can be calculated to determine the standard diagnosis name corresponding to the diagnosis name, and the diagnosis name is converted into ICD-10 codes according to a corresponding table of the standard diagnosis name and the ICD-10 codes.

After the diagnostic codes contained in the department have been obtained for each department, tf-idf for each diagnostic code can be calculated as shown in FIG. 1b, as will be described in more detail below.

Wherein tf is used to calculate a normalized value of the frequency of occurrence of a diagnostic code in a department. The normalized frequency tf _ij of diagnostic code i in department j can be calculated using equation 1 as follows:

in formula 1, i is department code, j is diagnostic code of disease type, n _ij is the number of times diagnostic code j occurs in department i, Σ _kn_ik is the total number of all diagnostic codes in department i.

Wherein idf is used to calculate the importance of a diagnostic code in distinguishing between different departments. If a diagnostic code is only present in a specific number of departments, it is considered that the diagnostic code may make a large contribution in distinguishing between the departments. The idf _j of diagnostic code j can be calculated using equation 2 as follows:

In equation 2, |D| is the total number of all department codes participating in the calculation, |{ i j ε D _i } | is the number of departments containing diagnostic code j, and D _i is the set of all diagnostic codes in department i. Based on equation 2, the smaller the number of departments containing the diagnosis code j, the larger the idf _j value of the diagnosis code, and thus the larger the weight of the diagnosis code j in distinguishing departments. For example, some diseases only occur in certain specialized departments, which have a large idf value, and by which these specialized departments can be effectively distinguished.

Based on the above, tf-idf _ij of diagnostic code j in department i is expressed as: tf-idf _ij＝tf_ij×idf_j, the code vector of department i is denoted v _i＝[tf-idf_i1,tf-idf_i2,tf-idf_i3,...,tf-idf_iN, where N is the Nth diagnostic code in department i and N is a positive integer.

Based on the above embodiment, the code vector of the current department of the second user and the code vector corresponding to each historical department of the second user can be obtained by calculation. Then, the similarity between the encoding vector of the current department and the encoding vector of each historical department is calculated.

Alternatively, in this embodiment, when calculating the similarity between the encoding vector of the current department and the encoding vector of each historical department, a cosine similarity calculation method may be used. For convenience of description, the coding vector of the current department is marked as v0, and the coding vector of a certain historical department is marked as v1, so that the similarity of the coding vector and the coding vector is the similarity of the coding vector and the coding vectorWherein v1·v0 represents the dot product of the encoded vector v1 and the encoded vector v 0. Based on the calculated similarity, a historical department adapted to the current department may be selected from at least one historical department as the first department.

When the query intention of the first user is estimated as follows: when the health data corresponding to the first historical visit department of the second user is queried, the health data generated by the second user when the first historical visit department is visited can be obtained from the health data corresponding to the second user and used as target health data.

A typical target health data acquisition method is shown in fig. 1c, and the first user's historical office for treatment comprises: historical office 1, historical office 2, historical office 3, historical office 4 …, each containing a different medical record. When the current visit department of the first user is obtained, the similarity between the current visit department and each historical visit department can be calculated respectively. As shown in fig. 1c, if the similarity between the current office and the historical office 1 and 3 is greater than 0.5, the historical office 1 and 3 may be considered as the first historical office. Next, medical records can be screened, medical records contained in the historical office 1 and the historical office 3 are used as target health data, and the target health data is sent to a client for display.

In an alternative embodiment, the first intent description data may include: historical query behavior of the first user. Based on at least one of query order, query frequency, and input query keywords of the first user when querying different health data in the historical time period, query preferences of the first user can be calculated. When the first user queries this time, the estimated query intention of the first user may be: and querying health data of a second user, wherein the health data is adapted to the query preference of the first user. For example, in the history inquiry process, a doctor prefers to inquire the history diagnosis and treatment scheme of a patient preferentially, and at this time, the doctor can consider that the attention of the doctor to the history diagnosis and treatment scheme of the patient is higher. Therefore, the query intention of the doctor at this time can be estimated to be related to the historical diagnosis and treatment scheme of the patient. And then, the historical diagnosis and treatment scheme can be obtained from the health data of the second user as target health data and sent to the client for display.

In an alternative embodiment, the first intent description data may include: at least one of a field in which the first user is adept, a historic diagnosis and treatment record, and the second intention description data may include a historic diagnosis and treatment disease of the second user. The estimated query intent of the first user may be: and inquiring health data corresponding to the first historical diseases of the second user.

In such an embodiment, it may be inferred which diseases the first user is about to diagnose based on the field of the first user's skill, the historic diagnosis. Next, among the historic medical treatment diseases of the second user, the historic medical treatment diseases adapted to the diseases to be diagnosed by the first user may be determined as the first historic diseases, and the first user may be estimated to request to query the health data corresponding to the first historic diseases of the second user. Next, the health data corresponding to the first historical disease may be obtained from the health data of the second user as target health data, and sent to the client for display.

In an alternative embodiment, the second intent description data may include: distribution characteristics of the health data of the second user. According to the second intention description data, the estimated query intention of the first user may be: and inquiring the health data of which the data volume duty ratio in the health data of the second user is larger than the set proportion threshold value. As described above, the distribution characteristics of the health data of the second user can be represented by the amount of data occupied by the medical data corresponding to the different disease types of the second user in the health data of the second user. If the ratio of the data amount of the medical data corresponding to one or more disease types in the health data of the second user is greater than a set proportion threshold (for example, 30 percent or 50 percent), the first user can be estimated to request to inquire the medical data. And then, the health data with the partial data volume of which the duty ratio is larger than the set proportion threshold value can be obtained as target health data so as to actively prompt the first user to pay attention to the medical data corresponding to the partial disease type of the second user.

In an alternative embodiment, the second intent description data may include: the time of generation of the different wellness data of the second user. According to the second intention description data, the estimated query intention of the first user may be: the health data generated by the second user over the specified historical period of time is queried. The designated historical time period refers to a historical time period before the second user makes a visit, for example, the first three months, half years or one year of the visit, which is not limited in this embodiment. When the query intention is estimated, the health data generated by the second user in the appointed historical time period can be obtained as target health data and sent to the client for display.

In an alternative embodiment, the second intent description data may include: the frequency of queries for different health data of the second user. According to the second intention description data, the estimated query intention of the first user may be: and inquiring health data with the inquiring frequency larger than the set frequency threshold value in the health data of the second user. The set frequency threshold may be set according to practical situations, which is not limited in this embodiment. When the query intention is estimated, the health data with the query frequency greater than the set frequency threshold value in the health data of the second user can be obtained as the target health data, and sent to the client 10 for display.

In some exemplary embodiments, the server 20 may further image the second user based on the health data of the second user, thereby facilitating the first user's quick knowledge of the second user's general health. The following will explain in detail.

In this embodiment, the server 20 may optionally identify the health features of the second user from the health data of the second user. Wherein the health feature may include: the present embodiment is not limited by one or more of the behavior characteristics of the doctor, the characteristics of the treatment means, the physical state characteristics, and the life habit characteristics. The server 20 may then generate at least one health description tag describing the above-described health features based on the identified health features to describe the general health of the second user via the at least one health description tag. The server 20 may then send the at least one health description tag to the client 10 for presentation.

It should be noted that, to optimize the display effect of the at least one health description tag on the client 10, the at least one health description tag may be divided into: the type of behavior label for consultation, the type of treatment label, the type of physical state, the type of life habit label, etc. Wherein each tag type may contain one or more health description tags.

An exemplary description will be made below.

Alternatively, the visit behavior feature may be expressed as: the number of visits by the second user within a set period of time, the time interval between adjacent visits, the medical facility at the visit, the department at the visit, etc. Accordingly, the following health description tags may be included under the visit behavior tag type: three months for 2 times of dermatology and 3 times of respiratory department in half a year, etc.

Alternatively, the treatment means may be characterized as: the procedure experienced by the second user, the medication used, the medical device implanted in the body, etc. Accordingly, under the treatment means tag types, the following health description tags may be included: heart bypass surgery, hip surgery, amoxicillin, ampicillin, etc., and implantation of cardiac pacemakers.

Alternatively, the physical state characteristics may be represented as: the second user's current illness, current physiological status, etc. Accordingly, under the body state label type, the following health description label may be included: type II diabetes, liver and kidney insufficiency, advanced pregnant women, etc.

Alternatively, the life habit features may be represented as: the living environment, working environment, eating habits, exercise habits, etc. of the second user. Accordingly, under the life habit tag type, the following health description tag may be included: takeaway is frequent, exercises once a week, sitting for 8 hours per day, etc.

After receiving the at least one health description tag, the client 10 may present the at least one health description tag in a tag presentation area. One exemplary way to present the health description tag in the tag presentation area may be as shown in fig. 2 a.

Based on this way of displaying the health description tag, on the one hand, based on at least one health description tag, the first user can be made to quickly learn about the general health of the second user; on the other hand, the first user may input his query intent through the at least one health description tag according to actual data requirements, as will be described in detail below.

In an alternative embodiment, the first user may initiate a selection operation on the at least one health description tag presented by the client 10. The client 10 acquires a health description tag selected by the first user from the at least one health description tag as a target health description tag, and transmits the target health description tag to the server 20.

Upon receiving the target health description tag, the server 20 may revise the pre-estimated query intent of the first user based on the target health description tag. It should be appreciated that after the query intent of the first user is revised, the server 20 may send the target health data adapted to the revised query intent of the first user to the client 10 to update the presentation content of the client 10 in real time.

Based on the above embodiments, the server 20 may further identify at least one disease type of the second user's historical visit based on the second user's health data. Alternatively, the disease type identification process may be implemented based on the disease label described in the foregoing embodiment. For example, the disease type of the second user's historical visit may be identified according to the cold label, rhinitis label, gastroenteritis label, coronary heart disease label corresponding to the health data as: cold, rhinitis, gastroenteritis, coronary heart disease, etc.

The server 20 may then divide the at least one disease type into at least one disease group by the disease group to which the disease type belongs. Alternatively, the dividing process of the disease group may be implemented based on the label of the disease group to which the disease described in the foregoing embodiment belongs in the medical field. For example, colds and rhinitis may be divided into respiratory disease groupings based on respiratory disease labels on cold labels and rhinitis labels; for example, gastroenteritis may be divided into groups of digestive systems according to digestive system disease tags on gastroenteritis tags; for another example, coronary heart disease may be divided into circulatory system groupings based on circulatory system disease labels on coronary heart disease.

The server 20 may then send at least one disease group and its corresponding number of visits, and the disease type and its corresponding number of visits under each disease group to the client 10 for presentation.

After receiving the data sent by the server 20, the client 10 may display at least one disease group and the corresponding number of visits included in the target health data in the medical history overview area. For example, icons such as "respiratory system (15)", "circulatory system (6)", "digestive system (4)", etc. as shown in fig. 2a may be displayed in the medical history overview area. The disease grouping and corresponding number of visits can facilitate the first user to quickly learn about the medical history of the second user

Alternatively, the client 10 may display at least one disease type and a corresponding number of visits under the target disease group in response to a selection operation for the target disease group in the at least one disease group. For example, after the first user clicks on "respiratory system (15)" in the medical history overview area, the client 10 may expand icons showing "cold (8)", "rhinitis (2)", "bronchitis (5)", etc. under the respiratory system as shown in fig. 2 b.

Optionally, the client 10 may also determine a target disease type selected by the first user in response to the selection operation for the at least one disease type and send the target disease type to the server 20.

After receiving the target disease type, the server 20 may modify the query intent of the first user according to the target disease type. It should be appreciated that after the query intent of the first user is revised, the server 20 may send the base health data adapted to the revised query intent of the first user to the client 10 to update the presentation content of the client 10 in real time.

Based on the above embodiments, when the server 20 sends the target health data to the client 10 for display, the target health data may be classified to obtain a plurality of data categories.

Optionally, in this embodiment, the plurality of data categories obtained by dividing may include: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category. The server 20 may then send the plurality of data types and the health data corresponding to each of the plurality of data types to the client 10.

After the client 10 acquires the plurality of data categories included in the target health data, the health data corresponding to the target data category in the plurality of data categories may be displayed in the medical history detail display area. Meanwhile, the client 10 may also display viewing icons corresponding to the data categories for the first user to switch the data categories.

Fig. 2a and 2b illustrate a typical target data class presentation. As shown in fig. 2a and 2b, the medical history detail display area is currently displaying a "comprehensive" view icon and comprehensive data under the icon, and simultaneously displaying view icons corresponding to other data categories such as "disease", "assay", "examination", "operation", "medicine", "epidemic prevention inoculation" and the like. If the first user wants to view the health data corresponding to the "surgery" category, the "surgery" viewing icon may be clicked to switch the health data corresponding to the "comprehensive" category being displayed to the health data corresponding to the "surgery" category.

The target data category may be a default data category, may be a first data category used for being preset, or may be a data category that the data processing system 100 preferentially recommends attention to the first user according to the viewing habit of the first user. The manner of determining the target data category will be exemplarily described below.

Alternatively, in one exemplary embodiment, the server 20 may obtain the first user's historical viewing behavior, which is then analyzed to obtain the first user's interest scores for the plurality of data categories. The interest score corresponding to each data category is in positive correlation with the frequency of the first user to preferentially check the health data in the data category in the history inquiry process.

Based on the interest scores of the plurality of data categories, the server 20 may determine, from the plurality of data categories, a data category for which the interest score satisfies the set condition as a data category adapted to the historical viewing behavior of the first user, and transmit an instruction to preferentially display the data category adapted to the historical viewing behavior of the first user to the client 10.

After receiving the instruction, the client 10 may determine a data category adapted to the historical viewing behavior of the first user as a target data category, and preferentially display the health data corresponding to the target data category in the medical history detail display area. For example, if a first user frequently reviews health data under the "medication" data category preferentially during historical queries, data processing system 100 may preferentially recommend exposing health data under the "medication" data category for the first user during current or future queries to optimize query efficiency.

It should be appreciated that the health data of the second user may include medical data generated by multiple visits by the user or physical examination reports generated by multiple physical examination. That is, the health data may include at least one medical history record associated with time. Wherein, the medical history refers to information for simply describing the case. Typically, a medical history may include a medical facility, department, type of visit (clinic, emergency, hospitalization).

Based on this, optionally, after the client 10 obtains the target health data, the at least one medical history record may be displayed in a time-axis display form in the medical history detail display area according to the respective corresponding time sequence of the at least one medical history record included in the target health data, so as to be convenient for viewing.

It should be noted that, when the client 10 displays a plurality of data categories included in the target health data, at least one medical history record under each data category may be displayed in a time axis. Meanwhile, when each medical history record in the target data category is displayed, the data tag of the detail data corresponding to the medical history record and the illness state description keywords of the user can be displayed. For example, as shown in fig. 2a and 2b, when the medical history record under the category of "comprehensive" data is displayed, the data labels of "anti-caries", "X-ray", "caries" and the like may be displayed in the case record of "first hospital/stomatology/clinic". For example, "condition" as shown in fig. 2a and 2 b: frequently toothache, and inability to eat spicy foods.

In some exemplary embodiments, the client 10 may prominently display a portion of the medical history record and fold-display another portion of the medical history record. The following will explain in detail.

Optionally, before the server 20 sends the target health data to the client 10 for display, the association degree between at least one medical history record included in the target health data and the query intention of the first user may be calculated respectively. After receiving the association degree corresponding to each of the at least one medical record, the client 10 may highlight the medical record having the association degree with the query intention of the first user greater than or equal to the set threshold value, and fold the medical record having the association degree with the query intention of the first user less than the set threshold value.

The server 20 may be implemented in connection with at least one of the intent description data described in the previous embodiments in calculating the relevance of each medical history record to the query intent of the first user. Taking any of the at least one medical history record as an example, the server 20 may optionally obtain the corresponding detail data for the medical history record. Wherein the detail data may include: the medical history record contains specific condition description data, specific diagnosis result data, specific prescription data, medical order data, and the like. Alternatively, the detail data may be from a case, medical record or medical record recorded by a doctor at the time of patient visit.

Then, the server 20 may calculate the relevance between the detail data and at least one type of intention description data, respectively, to obtain at least one relevance calculating result. Wherein the at least one intent description data may include: the first intention description data and/or the second intention description data are specifically referred to the description of the foregoing embodiments, and are not repeated herein. Alternatively, the server 20 may calculate the degree of correlation of the detail data and the intention description data by calculating the degree of correlation of the data tag corresponding to the detail data and the data tag corresponding to the intention description data, or may calculate the degree of correlation of the text of the detail data and the text of the intention description data based on a text recognition method, which is described herein in detail.

The server 20 may then weight and sum the at least one relevance score according to the weights corresponding to the at least one intent description data to obtain a relevance score of the medical history record to the query intent of the first user. Similarly, the server 20 may calculate the association between each medical history record and the query intention of the first user, and send the calculated association between the at least one medical history record and the client 10.

Fig. 2a and 2b illustrate a typical manner of displaying the medical records, and as shown in fig. 2a and 2b, a folding icon is displayed on the time axis, showing 99 medical records being folded. The second user may click on the collapse icon to view a collapsed medical history record.

Optionally, for any of the at least one medical history records, the client 10 may also display a detail view icon corresponding to the medical history record in a medical history detail display area, as shown in fig. 2a and 2 b. Further, if the first user wants to view details corresponding to the medical history record, the first user can trigger a detail viewing icon corresponding to the medical history record.

When the client 10 detects a trigger operation for the detail view icon, the detail page of the medical history record can be displayed in response to the trigger operation. On the details page of the medical history record, the client 10 may display the details data corresponding to the medical history record in text, picture, table and/or chart form. The pictures can be images of medical records prescribed by a medical institution. Wherein the chart may include, but is not limited to: bar graphs (histograms), line charts, pie charts, bar graphs, radar charts, etc., the present embodiment includes but is not limited to this.

FIG. 3a illustrates a piece of detail data of a history of "Xueconventional+liver function 3" under the data category of "test" shown in a table. As shown in fig. 3a, the table includes detailed data of each assay item and its corresponding measurement result, unit, reference value, etc.

FIG. 3b illustrates details of a medical history of "bacterial culture + drug susceptibility" under the data category of tabulated "assay". As shown in fig. 3b, the table includes detailed data of the authentication result, the drug susceptibility result, and the like.

Fig. 3c illustrates the detail data under the data category of "drug" presented using a bar graph. As shown in fig. 3c, the bar graph may show the type of medication administered to the patient over a period of time, the duration of the medication, and the conflict between medications. Each icon for displaying the duration of medication may display details of the duration of medication after the trigger information is detected, for example, a specific start date of medication, etc., which will not be described again.

In addition, as shown in fig. 3c, when the detailed data under the data category of "medicine" is displayed, allergy information of the patient may be further displayed. In fig. 3c, the allergy information of the patient can be displayed in a classified manner, so that the allergy condition of the patient can be known more clearly, and the doctor can take the prescription according to the symptoms and the allergy condition of the patient in the diagnosis.

It should be noted that in some exemplary embodiments, the client 10 may further highlight data that may be of greater interest to the first user or that may contribute more to the actual business of the first user when presenting the details page of the medical history. The following will explain in detail.

Optionally, in such an embodiment, server 20 may further obtain current sign information for the second user. The current symptom information may be manually entered by the first user, or collected by an audio device installed on the client 10, or actively acquired by the server 20. For example, when the second user is implemented as a patient visit, server 20 may obtain current sign information provided by the patient at the time of registration or triage via a third party registration platform.

For any of the at least one medical history records, the server 20 may employ a natural language processing algorithm (Natural Language Processing, NLP) to identify a portion of the detail data corresponding to the medical history record that matches the current symptom information of the second user, and then send the identification of the portion of detail data to the client 10.

After receiving the identifier of the part of detail data, the client 10 may highlight, in the detail page of the medical history record, part of detail data adapted to the current sign information of the second user in the detail data corresponding to the medical history record. Based on this embodiment, the first user can quickly acquire data that is of greater interest to him or data that contributes significantly to his actual business.

For example, the condition of the patient is cough, and the server 20 may determine drug information adapted to the cough condition from the detailed data corresponding to the patient's medical history based on a text recognition and matching algorithm, and transmit an identification of the drug information to the client 10. Further, the client 10 can highlight which drugs the patient took in the past to treat cough, so as to facilitate the doctor's diagnosis.

It should be noted that, based on the above embodiments, as shown in fig. 2a and fig. 2b, the client 10 may further display a filtering icon in the query setting area for the first user to input the filtering condition of the health data. Optionally, after the screening icon is triggered, the client 10 may display a screening interface, where the first user may input screening conditions such as a visit type, a medical institution, a medical department, and the like, which are not illustrated.

Based on the above embodiments, as shown in fig. 2a and fig. 2b, the client 10 may further display an optional health data query period in the query setup area for the first user to select. As shown in fig. 2a and 2b, the first user may set up to query for health data for all time periods, health data for the last three months, health data for the last six months, or health data for the last year.

Based on the above embodiments, as shown in fig. 2a and fig. 2b, the client 10 may display a query time customization control in the query setting area, so that the first user can customize the query time period of the health data.

Based on the above embodiments, as shown in fig. 2a and fig. 2b, the client 10 may display a search component in the query setting area, so that the first user may input a search keyword for the health data, which is not described herein.

In some exemplary embodiments, the server 20 may further calculate at least one diagnosis proposal for the second user according to the current sign information of the second user and the health data corresponding to the second user, and send the at least one diagnosis proposal to the client 10 for display. Optionally, the at least one visit proposal comprises: at least one of a consultation advice, an examination item advice, a medication intake advice, and a diagnosis advice associated with the second user.

For example, when the current symptom information of the first user is headache and fever, and the health data of the second user indicates that the frequency of the second-use cold is high, the inquiry advice calculated by the server 20 may include: whether the patient takes antipyretic drugs recently, whether the patient goes to dense places of people's flow recently, whether the patient has influenza at the side of the patient, and the like.

The client 10 may further present a suggestion presentation area. Alternatively, when the first user is implemented as a doctor and the second user is implemented as a patient, the client 10 may display at least one of the consultation advice, examination item advice, medication intake advice, and diagnosis advice associated with the second user, transmitted by the server 20, at the advice display area. The diagnosis proposal can assist the first user to quickly determine the diagnosis direction of the second user, thereby realizing the efficient diagnosis process.

In some exemplary embodiments, data processing system 100 may set different query terms for different querying users. For example, for a doctor, its query authority may be set as follows: and inquiring health data corresponding to a department to which the doctor belongs in the health data of the user, or inquiring health data matched with the main treatment field of the doctor in the health data of the user. For example, for pharmacy staff, the query authority may be set as follows: and inquiring data related to the diagnosis and treatment prescription in the health data of the user. For example, for a medical patch processing user, its query permissions may be set as: and inquiring medical records of diseases meeting medical subsidy conditions and data related to the charging records in the health data of the user. For example, for a work injury reimbursement processor, the query authority can be set as follows: and inquiring relevant data of the diseases authenticated as industrial injury in the health data of the user. For another example, for a underwriter and a claims processor of a commercial insurance company, its query permissions may be set as: the related data of medical records affecting the applied past medical history in the health data of the user or the related data of diseases for which the user applies for claims are queried, and are not repeated.

Based on this, after receiving the query operation of the first user, the client 10 may acquire the identity information of the first user, and send the identity information of the first user to the server 20. Typically, the identity information may include a query account number that is pre-registered and authorized for use. The server 20 may determine the query authority of the first user according to the identity information of the first user. Then, determining the health data matched with the query authority of the first user from the health data corresponding to the second user, and acquiring the health data matched with the query intention of the first user from the health data matched with the query authority of the first user, wherein the health data is used as target health data and is not described in detail.

In some exemplary embodiments, a first user may be implemented as a second user, who may learn their own health data based on the data processing system 100 provided by the previous embodiments. Further, data processing system 100 may also provide health alert functions to the user, as will be described by way of example.

In some alternative embodiments, the user may enter his or her current medication through the client 10. The client 10 or the server 20 may query the administration period and the administration number of the medicine after acquiring the medicine, and calculate the administration time of the medicine based on the administration period and the administration number. Next, when the administration time of the drug arrives, an administration reminding message may be outputted by the client 10 to remind the user to administer the drug at an appropriate time to perfect the diagnosis and treatment process.

Optionally, the medicine taking reminding message output by the client 10 may indicate that the medicine taking time of the user arrives, please take the medicine on time, and further remind the user of the quantity of medicine taking, the taking notice (warm/cold water, before/after meals) and the like, which will not be described again.

In other alternative embodiments, the client 10 may also present health advice for the second user. Wherein the health advice may be generated by the server 20 based on physical examination report data of the second user. For example, if the physical examination report of the second user indicates that the liver health of the second user is impaired, the client 10 may prompt the second user to avoid staying up at night as much as possible, and drink a small amount of wine, etc.; for another example, if the physical examination report of the second user indicates that the immunity of the second user is poor, the client 10 may prompt the user to exercise his body, balance his diet, etc., which will not be repeated.

It should be noted that, in addition to performing the health data display operation according to the logic described in the foregoing embodiments, the data processing system 100 according to the embodiment of the present application may also perform the health data display operation according to other alternative logic methods, which will be described in the following exemplary embodiments.

Alternatively, data processing system 100 may categorize querying users, and each category of querying users may query for different health data. In order to facilitate classification of the querying user, a dedicated account or an identity identifier can be allocated to different types of querying users as identity information of the querying user. When a user to be queried initiates a query request, the query intention of the user to be queried can be identified based on the identity information of the user to be queried. The description will be continued taking the first user and the second user as examples.

The identity information of the first user may be provided when the first user initiates an operation to query the health data of the second user via the client 10. For example, when the first user initiates a query operation, a login account and a password, or other authorized identification, may be provided as the identity information.

After the client 10 acquires the identity information of the first user, the identity information may be added to the data acquisition request, and the data acquisition request may be sent to the server 20.

When the server 20 predicts the query intention of the first user according to the data acquisition request sent by the client 10, the identity information of the first user carried by the data acquisition request can be acquired, and the query intention of the first user can be predicted according to the identity information of the first user.

In some alternative embodiments, if the server 20 identifies that the first user is a medical subsidy processing user according to the identity information of the first user, the server 20 may predict that the query intention of the first user is: querying the target disease type meeting the medical subsidy condition.

Alternatively, the medical subsidy processing user may be assigned a corresponding medical subsidy processing account number and a password in advance, and when the server 20 recognizes that the identity information input by the first user includes the medical subsidy processing account number and the password is matched with the account number, it may be determined that the first user is the medical subsidy processing user. Or the identification mark such as the work number, the organization code or the identification number of the medical subsidy processing user can be input in advance, and the user is prompted to input the identification mark when the first user initiates the inquiry operation. After the identification mark input by the user is obtained, consistency verification can be performed according to the pre-input identification mark, and when the consistency verification passes, the first user is determined to be a medical subsidy processing user.

Based on this, when the server 20 acquires the health data adapted to the query intention of the first user from the health data corresponding to the second user, the diagnosis and treatment record and the diagnosis and treatment cost data corresponding to the target disease type satisfying the medical subsidy condition can be acquired as the target health data from the health data of the second user. The server 20 may then send the target health data to the client 10 for presentation. The medical subsidy conditions are preset, and the medical subsidy conditions of different regions or different administrative areas are different and can be differently set according to the region or administrative area to which the user belongs, which is not repeated in this embodiment.

Based on the above, the disease type corresponding to the second user can be obtained from the health data of the second user, and then the disease type satisfying the preset medical patch condition is determined from the disease types. And then, acquiring diagnosis and treatment records and diagnosis and treatment cost corresponding to the disease type for display so as to be checked by a first user.

The embodiment has the advantages that a channel for quickly inquiring the health data of the subsidized user is provided for the medical subsidized user, so that the processing efficiency of the medical subsidized is improved, and the true reliability of the diagnosis and treatment record of the subsidized user is ensured.

In other alternative embodiments, the server 20 identifies the first user as a business insurance processing user based on the identity information of the first user, and then the server 20 may predict the query intent of the first user as: health data associated with the business insurance is queried. Wherein, business insurance processing users can be realized as operators of business insurance companies. When the second user applies for insurance, renewal or insurance claim services, the first user may query the health data of the second user to ensure that the services can be accurately and efficiently provided to the second user.

Alternatively, the health data associated with the business insurance may include: health data when the second user applies for insurance, health data when the second user continues applying for insurance claims, or health data when the second user applies for insurance claims.

Alternatively, a specific query for which type of health data associated with business insurance is described above may be provided actively by the first user. For example, the multiple query intent options described above may be presented for user input when a first user initiates a query operation, or an intent input box may be presented for user input. Alternatively, since the service contents handled by different operators are different, different account numbers may be allocated to operators handling different services in this embodiment. For example, an application verification account is assigned to a user who handles the application verification, and an claim verification account is assigned to a user who handles the insurance claim verification. Based on the above, when the first user initiates the query operation, the account number of the first user can be obtained, and based on the service type corresponding to the account number, the specific query intention of the first user can be obtained.

Based on the above, when the server 20 obtains the health data adapted to the query intention of the first user from the health data corresponding to the second user, the authorization request may be sent to the second user in advance according to the query intention of the first user. Alternatively, in this embodiment, the query intention of the first user may be sent to the first user in the form of a short message, a telephone or a terminal push message, so as to request the first user to authorize the action. And when the second user returns an authorization notification message according to the authorization request, displaying data matched with the query intention of the first user in the health data of the second user.

If an authorization notification message returned by the second user according to the authorization request is received, the server 20 acquires health data associated with the business insurance from the health data of the second user. Based on the mode of requesting authorization from the user, the efficiency of application verification, renewal verification and claim verification can be effectively improved while the information security of the user is ensured.

In still other alternative embodiments, if the server 20 identifies that the first user is a disease control user with a set geographical area according to the identity information of the first user, the server 20 may predict that the query intention of the first user is: requesting to inquire the disease distribution characteristics within the set region range.

Wherein, the disease control user can be a staff of a disease control center or a health epidemic prevention department. Based on the present embodiment, prevention of a specific disease (e.g., epidemic disease) can be facilitated. The set area range may be an area range obtained by dividing the set area range according to a geographical area, or an area range obtained by dividing the set area range according to an administrative area, which is not limited in this embodiment.

Based on the above, when the server 20 obtains the health data adapted to the query intention of the first user from the health data corresponding to the second user, it can obtain the medical data of the second user in the plurality of medical institutions belonging to the set region range, and calculate the disease distribution characteristics in the set region range according to the medical data. Wherein the second user may comprise a plurality of users.

Alternatively, for each geographic area, the server 20 may analyze the frequency of visits for each disease within that geographic area. Based on the number of visits, the high incidence diseases in the region range can be determined, so that relevant departments can assign and take corresponding disease control means.

Alternatively, for each geographic area, the server 20 may analyze the period of visit for each disease within that geographic area. Based on the visit period, the open time of the medical institution and the ratio of the personnel on the duty can be adjusted to meet the visit requirement.

Alternatively, for each geographic area, server 20 may analyze the medical facilities for each disease within that geographic area. Based on the analysis result of the medical institution, the medical institution with larger treatment flow can be subjected to measures such as diversion and diversion.

Alternatively, for each geographical range, the server 20 may analytically calculate the age of onset of each disease within that geographical range. Based on the age group analysis results, the age group of the high incidence of the disease can be determined, which is beneficial to developing new medicines, adjusting medical resource distribution and the like.

After the disease distribution characteristics within the set region range are calculated, the disease distribution characteristics may be sent to the client 10 for visual display. Optionally, when the client 10 visually displays the disease distribution characteristics, the client may display the disease distribution characteristics through various data charts, for example, a line graph, a pie chart, a bar chart, etc., which are not repeated.

Optionally, in this embodiment, the server 20 may further calculate a target disease with a frequency of visits greater than the set frequency threshold according to the disease distribution characteristics, and output an early warning message for the target disease. The method for outputting the early warning message may include: the early warning message for the target disease is sent to the client 10, the early warning short message for the target disease is sent to the user in the set region range, the early warning message for the target disease is pushed to the terminal of the user in the set region range, or the disease epidemic prevention early warning notification can be sent to the disease control department in the set region range, which is not repeated.

Based on the scheme provided by the embodiment, disease control users can find out the disease distribution characteristics of different areas in time, and specific diseases (such as epidemic diseases) can be prevented conveniently.

In addition to the data processing system described in the above embodiments, embodiments of the present application further provide a data processing method, which will be described in detail below.

Fig. 4 is a flowchart of a data processing method according to an exemplary embodiment of the present application, which when executed on a client side, may include the steps shown in fig. 4:

Step 401, responding to the operation of the first user for inquiring the health data of the second user, and sending a corresponding data acquisition request to the server.

Step 402, receiving target health data of the second user returned by the server according to the data acquisition request.

Step 403, displaying the target health data, where the target health data is adapted to the query intention of the first user estimated by the server.

In some exemplary embodiments, the method further comprises: displaying at least one health description tag of the second user in a tag display area for selection by the first user; acquiring a target health description tag selected by the first user from the at least one health description tag; and sending the target health description tag to the server so that the server corrects the query intention of the first user according to the target health description tag.

In some exemplary embodiments, a way of presenting the target health data includes: displaying at least one disease group and corresponding visit times contained in the target health data in a medical history overview area; in response to a selection operation for a target disease group of the at least one disease group, at least one disease type and a corresponding number of visits under the target disease group are displayed.

In some exemplary embodiments, the method further comprises: determining the first user selected target disease type in response to a selection operation for the at least one disease type; the target disease type is sent to the server, so that the server corrects the query intention of the first user according to the target disease type.

In some exemplary embodiments, a way of presenting the target health data includes: acquiring a plurality of data categories contained in the target health data; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category; and displaying the health data corresponding to the target data category in the plurality of data categories in the medical history detail display area, and displaying the view icons corresponding to the plurality of data categories for the first user to switch the data categories.

In some exemplary embodiments, a way of presenting health data corresponding to a target data category of the plurality of data categories includes: acquiring a data category which is indicated by the server and is matched with the historical viewing behavior of the first user, and taking the data category as the target data category; and preferentially displaying the health data corresponding to the target data category in the medical history detail display area.

In some exemplary embodiments, a way of presenting the target health data includes: acquiring at least one medical history record contained in the target health data; and displaying the at least one medical history record in a time-axis display form in the medical history detail display area according to the corresponding treatment time sequence of the at least one medical history record.

In some exemplary embodiments, the at least one medical history record is presented in a presentation form of a timeline, further comprising: the medical history records with the degree of association with the query intent of the first user greater than or equal to a set threshold are highlighted, and the medical history records with the degree of association with the query intent of the first user less than the set threshold are folded.

In some exemplary embodiments, for any of the at least one medical history record, further comprising: displaying a detail view icon corresponding to the medical history record in the medical history detail display area; responding to the triggering operation aiming at the detail checking icon, and displaying the detail page of the medical history record; and displaying the detail data corresponding to the medical history record in the form of text, pictures, tables and/or charts on the detail page of the medical history record.

In some exemplary embodiments, the method further comprises: acquiring current symptom information of a second user; and highlighting part of detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history record in the detail page of the medical history record.

In some exemplary embodiments, the method further comprises: displaying a screening icon in the query setting area for the first user to input screening conditions of the health data; and/or displaying an optional health data query time period in the query setting area for selection by the first user; and/or displaying a query time custom control in the query setting area for the first user to customize a query time period of the health data; and/or displaying a search component in the query setup area for the first user to enter search keywords for the health data.

In some exemplary embodiments, the method further comprises: at least one of a consultation suggestion, an examination item suggestion, a medication intake suggestion and a diagnosis suggestion, which are associated with the second user and transmitted by the server, is displayed in a suggestion display area.

In this embodiment, when the first user initiates the operation of checking the health data of the second user, the client may display the target health data adapted to the query intention of the first user in the health data of the second user, so as to implement intelligent recommendation of the health data, thereby being beneficial to quickly displaying the health data meeting the query requirement to the first user and improving the data query efficiency.

Fig. 5 is a flowchart of a data processing method according to another exemplary embodiment of the present application, which when executed on a server side, may include the steps shown in fig. 5:

step 501, a data acquisition request is received, where the data acquisition request is sent by a client when a first user queries health data of a second user.

Step 502, estimating the query intention of the first user according to the data acquisition request.

Step 503, obtaining health data adapted to the query intention of the first user from the health data corresponding to the second user, as target health data.

Step 504, the target health data is sent to the client for display.

In some exemplary embodiments, one way of predicting the first user's query intent based on the data acquisition request includes: acquiring first intention description data associated with the first user and/or second intention description data associated with the second user according to the data acquisition request; estimating the query intention of the first user according to the first intention description data and/or the second intention description data.

In some exemplary embodiments, in accordance with the data acquisition request, one way of acquiring first intent description data associated with the first user includes: extracting an identification of the first user from the data acquisition request; acquiring at least one of attribute information, historical query behavior and preset query conditions of the first user according to the identification of the first user; wherein the attribute information includes: at least one of affiliated medical institutions, affiliated departments, good areas, indications, historic diagnosis and treatment records.

In some exemplary embodiments, in accordance with the data acquisition request, one way of acquiring second intent description data associated with the second user includes: extracting an identification of the second user from the data acquisition request; and acquiring at least one of a historical visit department, a historical visit disease, a distribution characteristic of health data, generation time of different health data and query frequency of different health data of the second user according to the identification of the second user.

In some exemplary embodiments, a way of predicting a query intent of the first user based on the first intent description data and/or the second intent description data, comprises: estimating, based on the first intent description data and/or the second intent description data, at least one of the following query intents of the first user: inquiring health data corresponding to a first historical visit department of the second user; inquiring health data corresponding to the first historical disease of the second user; querying health data adapted to the query preferences of the first user; inquiring health data with the data volume of which the duty ratio is larger than a set proportion threshold value in the health data of the second user; querying health data generated by the second user during a specified historical period of time; and inquiring the health data with the inquiring frequency larger than the set frequency threshold value in the health data of the second user.

In some exemplary embodiments, estimating a way for the first user to query health data corresponding to a first historical visit department of the second user based on the first intent description data and/or the second intent description data comprises: acquiring a department affiliated by the first user from the first intention description data as a current consultation department of the second user; acquiring a department corresponding to at least one history visit record of the second user from the second intention description data as at least one history visit department of the second user; respectively calculating the similarity of the current visit department and the at least one historical visit department; from the at least one historical visit department, a historical visit department with a similarity to the current visit department greater than a set similarity threshold is selected as the first historical visit department.

In some exemplary embodiments, one way of separately calculating the similarity of the current visit department and the at least one historical visit department includes: obtaining a code vector corresponding to the current department of medical treatment according to the diagnosis code of the disease type contained in the current department of medical treatment and tf-idf corresponding to the diagnosis code; acquiring the respective corresponding coding vector of the at least one historical visit department; and respectively calculating the similarity of the coding vector corresponding to the current visit department and the coding vector corresponding to each of the at least one historical visit department to obtain the similarity of the current visit department and the at least one historical visit department.

In some exemplary embodiments, the method further comprises: acquiring a target health description tag sent by the client; the target health description tag is selected by the first user from at least one health description tag of the second user; and correcting the query intention of the first user according to the target health description tag.

In some exemplary embodiments, the method further comprises: identifying health features of the second user based on the health data of the second user; the medical health comprises: one or more of a visit behavior feature, a treatment means feature, a physical state feature, a life habit feature; generating at least one health description tag for the second user based on the health characteristics; and sending the at least one health description tag to the client for display, so that the first user can select the target health description tag from the at least one health description tag.

In some exemplary embodiments, the method further comprises: obtaining a target disease type sent by the client; the target disease type is selected by the first user from at least one disease type; and correcting the query intention of the first user according to the target disease type.

In some exemplary embodiments, the method further comprises: identifying at least one disease type of the second user's historical visit based on the second user's health data; dividing the at least one disease type into at least one disease group according to the disease group to which the disease type belongs; and sending the at least one disease group and the corresponding visit times thereof, and the disease type and the corresponding visit times thereof under each disease group to the client for display so that the first user can select the target disease type from the first user.

In some exemplary embodiments, one way of sending the target health data to the client for presentation includes: classifying the target health data to obtain a plurality of data classes; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category; and sending the health data corresponding to each of the data categories to the client for display.

In some exemplary embodiments, the method further comprises: acquiring historical viewing behaviors of the first user; analyzing the historical viewing behavior of the first user to obtain interest scores of the first user for the plurality of data categories; determining a data category with interest scores meeting a set condition from the plurality of data categories as a data category adapted to the historical viewing behavior of the first user; and sending an instruction to the client to preferentially display the data category matched with the historical viewing behavior of the first user.

In some exemplary embodiments, before sending the target health data to the client for presentation, the method further includes: calculating the association degree of at least one medical history record and the query intention of the first user according to at least one medical history record contained in the target health data; and sending the calculated relevance degree corresponding to each at least one medical history record to the client so that the client can prominently display the medical history records with relevance degrees larger than or equal to a set threshold.

In some exemplary embodiments, one way of separately calculating the relevance of the at least one medical history record to the first user's query intent includes: acquiring detail data corresponding to any one of the at least one medical history record; respectively calculating the relevance between the detail data and at least one type of intention description data to obtain at least one relevance calculation result; the at least one intent description data includes: the first intent description data and/or the second intent description data; and according to the weights corresponding to the at least one intention description data, carrying out weighted summation on the at least one correlation calculation result to obtain the correlation between the medical history record and the query intention of the first user.

In some exemplary embodiments, the method further comprises: acquiring current sign information of the second user; identifying partial detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history record by adopting a natural language processing algorithm aiming at any medical history record in the at least one medical history record; and sending the identification mark of the part of detail data to the client so that the client prominently displays the part of detail data.

In some exemplary embodiments, the method further comprises: calculating at least one diagnosis proposal aiming at the second user according to the current symptom information of the second user and the corresponding health data of the second user, and sending the at least one diagnosis proposal to the client for display; wherein the at least one visit proposal comprises: at least one of a consultation advice, an examination item advice, a medication intake advice, and a diagnosis advice associated with the second user.

In some exemplary embodiments, one way of predicting the first user's query intent based on the data acquisition request includes: acquiring identity information of the first user carried by a data acquisition request; and estimating the query intention of the first user according to the identity information of the first user.

In some exemplary embodiments, a way of estimating a query intent of the first user based on identity information of the first user includes: if the first user is identified as a medical subsidy processing user according to the identity information of the first user, estimating that the first user requests to inquire the target disease type meeting the medical subsidy condition; obtaining health data adapted to the query intention of the first user from the health data corresponding to the second user, including: and acquiring diagnosis and treatment records and diagnosis and treatment expense data corresponding to the target disease types meeting the medical subsidy condition from the health data of the second user.

In some exemplary embodiments, a way of estimating a query intent of the first user based on identity information of the first user includes: if the first user is identified as a business insurance processing user according to the identity information of the first user, estimating that the first user requests to inquire health data associated with business insurance; obtaining health data adapted to the query intention of the first user from the health data corresponding to the second user, including: sending an authorization request to the second user according to the query intention of the first user; and if the authorization notification message returned by the second user according to the authorization request is received, acquiring health data associated with business insurance from the health data of the second user.

In some exemplary embodiments, the health data associated with the business insurance includes: the health data of the second user when the second user applies for insurance claims, the health data of the second user when the second user continues applying for insurance claims, or the health data of the second user when the second user applies for insurance claims.

In some exemplary embodiments, a way of estimating a query intent of the first user based on identity information of the first user includes: if the first user is identified as a disease control user with a set region range according to the identity information of the first user, estimating that the first user requests to inquire the disease distribution characteristics in the set region range; obtaining health data adapted to the query intention of the first user from the health data corresponding to the second user, including: medical data of the second user in a plurality of medical institutions belonging to the set region range is obtained, and disease distribution characteristics in the set region range are calculated according to the medical data.

In some exemplary embodiments, the disease distribution characteristics include at least one of: frequency of visit for each disease, period of visit for each disease, medical institution for each disease, age of onset for each disease.

In some exemplary embodiments, the method further comprises: calculating a target disease with a frequency of visits greater than a set frequency threshold according to the disease distribution characteristics; and outputting an early warning message aiming at the target disease to the client.

In some exemplary embodiments, obtaining health data adapted to the query intent of the first user from the health data corresponding to the second user as one way of targeting the health data includes: determining the query authority of the first user according to the identity information of the first user; determining health data adapted to the query authority of the first user from the health data corresponding to the second user; and acquiring the health data matched with the query intention of the first user from the health data matched with the query authority of the first user as target health data.

In some exemplary embodiments, the health data corresponding to the second user includes: medical data generated by the second user when medical institutions belonging to different regions perform diagnosis and treatment; and/or medical data generated by the second user when a department affiliated with a different medical institution makes a diagnosis.

In this embodiment, when the first user initiates the operation of checking the health data of the second user, the query intention of the first user may be estimated, and the target health data adapted to the query intention of the first user in the health data of the second user may be returned to the client, so as to display the target health data through the client, thereby implementing intelligent recommendation of the health data, facilitating quick display of the health data meeting the query requirement thereof to the first user, and improving the data query efficiency.

It should be noted that, the execution subjects of each step of the method provided in the above embodiment may be the same device, or the method may also be executed by different devices. For example, the execution subject of steps 201 to 204 may be device a; for another example, the execution subject of steps 201 and 202 may be device a, and the execution subject of step 203 may be device B; etc.

In addition, in some of the flows described in the above embodiments and the drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that the operations may be performed out of the order in which they appear herein or performed in parallel, the sequence numbers of the operations such as 201, 202, etc. are merely used to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

Fig. 6 is a schematic structural diagram of a health data display device according to an exemplary embodiment of the present application, and as shown in fig. 6, the device may include:

the request module 601 is configured to respond to an operation of the first user for querying health data of the second user, and send a corresponding data acquisition request to the server.

And the receiving module 602 is configured to receive target health data of the second user returned by the server according to the data acquisition request.

The display module 603 is configured to display the target health data, where the target health data is adapted to the query intention of the first user estimated by the server.

Further optionally, the presentation module 603 includes a label presentation sub-module 603a; the label presentation sub-module 603a is for: displaying at least one health description tag of the second user for selection by the first user; a target health description tag selected by the first user from the at least one health description tag is obtained.

Further optionally, the apparatus further comprises: a transmitting module 604; the sending module 604 is configured to: and sending the target health description tag to the server so that the server corrects the query intention of the first user according to the target health description tag.

Further optionally, the presentation module 603 includes a medical history overview sub-module 603b; the medical history overview sub-module 603b is for: displaying at least one disease group and corresponding visit times contained in the target health data in a medical history overview area; in response to a selection operation for a target disease group of the at least one disease group, at least one disease type and a corresponding number of visits under the target disease group are displayed.

Further optionally, the medical history overview submodule 603b is further configured to: determining the first user selected target disease type in response to a selection operation for the at least one disease type; the sending module 604 is further configured to: the target disease type is sent to the server, so that the server corrects the query intention of the first user according to the target disease type.

Further optionally, the presentation module 603 includes a medical history detail presentation sub-module 603c; medical history details presentation submodule 603c for: acquiring a plurality of data categories contained in the target health data; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category; and displaying the health data corresponding to the target data category in the plurality of data categories, and displaying the view icons corresponding to the plurality of data categories for the first user to switch the data categories.

Further optionally, when the medical history detail displaying submodule 603c displays health data corresponding to a target data category in the plurality of data categories, the medical history detail displaying submodule is specifically configured to: acquiring a data category which is indicated by the server and is matched with the historical viewing behavior of the first user, and taking the data category as the target data category; and preferentially displaying the health data corresponding to the target data category in the medical history detail display area.

Further optionally, when the medical history detail display sub-module 603c displays the target health data, the method specifically is used for: acquiring at least one medical history record contained in the target health data; and displaying the at least one medical history record in a time-axis display form in the medical history detail display area according to the corresponding treatment time sequence of the at least one medical history record.

Further optionally, the medical history detail presentation submodule 603c is further configured to, when presenting the at least one medical history record in a presentation form of a time axis: the medical history records with the degree of association with the query intent of the first user greater than or equal to a set threshold are highlighted, and the medical history records with the degree of association with the query intent of the first user less than the set threshold are folded.

Further optionally, for any of the at least one medical history records, the medical history detail presentation submodule 603c is further configured to: displaying a detail view icon corresponding to the medical history record in the medical history detail display area; responding to the triggering operation aiming at the detail checking icon, and displaying the detail page of the medical history record; and displaying the detail data corresponding to the medical history record in the form of text, pictures, tables and/or charts on the detail page of the medical history record.

Further optionally, the medical history detail presentation submodule 603c is further configured to: acquiring current sign information of the second user; and highlighting part of detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history record in the detail page of the medical history record.

Further optionally, the apparatus further comprises: a query setup module 605; the query setting module 605 is specifically configured to: displaying a screening icon for the first user to input screening conditions of the health data; and/or displaying an optional health data query time period for selection by the first user; and/or displaying a query time custom control for the first user to customize a query time period of the health data; and/or, presenting a search component for the first user to enter search keywords for the health data.

Further optionally, the display module 603 is further configured to: at least one of a consultation suggestion, an examination item suggestion, a medication intake suggestion and a diagnosis suggestion, which are associated with the second user and transmitted by the server, is displayed in a suggestion display area.

Further optionally, the health data display device may further include: a login module 606, a user information display module 607, and other modules. Only some of the modules are schematically shown in fig. 6 and are not meant to include only the modules shown in fig. 6.

In this embodiment, the health data display device may display, when the first user initiates the operation of viewing the health data of the second user, the target health data adapted to the query intention of the first user in the health data of the second user, so as to implement intelligent recommendation of the health data, thereby being beneficial to rapidly displaying the health data meeting the query requirement of the first user, and improving the data query efficiency.

Fig. 7 is a schematic structural diagram of a health data presentation device according to another exemplary embodiment of the present application, as shown in fig. 7, the device includes:

The receiving module 701 is configured to receive a data acquisition request, where the data acquisition request is sent by the client when the first user queries the health data of the second user.

The intent estimating module 702 is configured to estimate a query intent of the first user according to the data acquisition request.

The data obtaining module 703 is configured to obtain, from the health data corresponding to the second user, health data adapted to the query intention of the first user, as target health data.

And the sending module 704 is configured to send the target health data to the client for display.

Further optionally, the intent estimation module 702 is specifically configured to, when estimating the query intent of the first user according to the data acquisition request: acquiring first intention description data associated with the first user and/or second intention description data associated with the second user according to the data acquisition request; estimating the query intention of the first user according to the first intention description data and/or the second intention description data.

Further optionally, the intent estimation module 702 is specifically configured to, when acquiring the first intent description data associated with the first user according to the data acquisition request: extracting an identification of the first user from the data acquisition request; acquiring at least one of attribute information, historical query behavior and preset query conditions of the first user according to the identification of the first user; wherein the attribute information includes: at least one of affiliated medical institutions, affiliated departments, good areas, indications, historic diagnosis and treatment records.

Further optionally, the intent estimation module 702 is specifically configured to, when acquiring the second intent description data associated with the second user according to the data acquisition request: extracting an identification of the second user from the data acquisition request; and acquiring at least one of a historical visit department, a historical visit disease, a distribution characteristic of health data, generation time of different health data and query frequency of different health data of the second user according to the identification of the second user.

Further optionally, the intent estimation module 702 is specifically configured to, when estimating the query intent of the first user according to the first intent description data and/or the second intent description data: estimating, based on the first intent description data and/or the second intent description data, at least one of the following query intents of the first user: inquiring health data corresponding to a first historical visit department of the second user; inquiring health data corresponding to the first historical disease of the second user; querying health data adapted to the query preferences of the first user; inquiring health data with the data volume of which the duty ratio is larger than a set proportion threshold value in the health data of the second user; querying health data generated by the second user during a specified historical period of time; and inquiring the health data with the inquiring frequency larger than the set frequency threshold value in the health data of the second user.

Further optionally, the intention estimating module 702 is specifically configured to, when estimating, according to the first intention description data and/or the second intention description data, that the first user queries the health data corresponding to the first historical office of medical care of the second user: acquiring a department affiliated by the first user from the first intention description data as a current consultation department of the second user; acquiring a department corresponding to at least one history visit record of the second user from the second intention description data as at least one history visit department of the second user; respectively calculating the similarity of the current visit department and the at least one historical visit department; from the at least one historical visit department, a historical visit department with a similarity to the current visit department greater than a set similarity threshold is selected as the first historical visit department.

Further optionally, the intention prediction module 702 is specifically configured to, when calculating the similarity between the current office and the at least one historical office, respectively: obtaining a code vector corresponding to the current department of medical treatment according to the diagnosis code of the disease type contained in the current department of medical treatment and tf-idf corresponding to the diagnosis code; acquiring the respective corresponding coding vector of the at least one historical visit department; and respectively calculating the similarity of the coding vector corresponding to the current visit department and the coding vector corresponding to each of the at least one historical visit department to obtain the similarity of the current visit department and the at least one historical visit department.

Further optionally, the receiving module 701 is further configured to: acquiring a target health description tag sent by the client; the target health description tag is selected by the first user from at least one health description tag of the second user; the intent estimation module 702 is also configured to: and correcting the query intention of the first user according to the target health description tag.

Further optionally, the apparatus further comprises a data preprocessing module 705; the data preprocessing module 705 is specifically configured to: identifying health features of the second user based on the health data of the second user; the medical health comprises: one or more of a visit behavior feature, a treatment means feature, a physical state feature, a life habit feature; generating at least one health description tag for the second user based on the health characteristics; the sending module 704 is further configured to: and sending the at least one health description tag to the client for display, so that the first user can select the target health description tag from the at least one health description tag.

Further optionally, the receiving module 701 is further configured to: obtaining a target disease type sent by the client; the target disease type is selected by the first user from at least one disease type; the intent estimation module 702 is also configured to: and correcting the query intention of the first user according to the target disease type.

Further optionally, the data preprocessing module 705 is further configured to: identifying at least one disease type of the second user's historical visit based on the second user's health data; dividing the at least one disease type into at least one disease group according to the disease group to which the disease type belongs; the sending module 704 is further configured to: and sending the at least one disease group and the corresponding visit times thereof, and the disease type and the corresponding visit times thereof under each disease group to the client for display so that the first user can select the target disease type from the first user.

Further optionally, when the data preprocessing module 705 sends the target health data to the client for display, the data preprocessing module is specifically configured to: classifying the target health data to obtain a plurality of data classes; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category; the sending module 704 is further configured to: and sending the health data corresponding to the data categories to the client for display.

Further optionally, the data preprocessing module 705 is further configured to: acquiring historical viewing behaviors of the first user; analyzing the historical viewing behavior of the first user to obtain interest scores of the first user for the plurality of data categories; determining a data category with interest scores meeting a set condition from the plurality of data categories as a data category adapted to the historical viewing behavior of the first user; the sending module 704 is further configured to: and sending an instruction to the client to preferentially display the data category matched with the historical viewing behavior of the first user.

Further optionally, before the data preprocessing module 705 sends the target health data to the client for presentation, the data preprocessing module is further configured to: calculating the association degree of at least one medical history record and the query intention of the first user according to at least one medical history record contained in the target health data; the sending module 704 is further configured to: and sending the calculated relevance degree corresponding to each at least one medical history record to the client so that the client can prominently display the medical history records with relevance degrees larger than or equal to a set threshold.

Further optionally, the data preprocessing module 705 is specifically configured to, when calculating the association degree between the at least one medical history record and the query intention of the first user, respectively: acquiring detail data corresponding to any one of the at least one medical history record; respectively calculating the relevance between the detail data and at least one type of intention description data to obtain at least one relevance calculation result; the at least one intent description data includes: the first intent description data and/or the second intent description data; and according to the weights corresponding to the at least one intention description data, carrying out weighted summation on the at least one correlation calculation result to obtain the correlation between the medical history record and the query intention of the first user.

Further optionally, the data preprocessing module 705 is further configured to: acquiring current sign information of the second user; identifying partial detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history record by adopting a natural language processing algorithm aiming at any medical history record in the at least one medical history record; the sending module 704 is further configured to: and sending the identification mark of the part of detail data to the client so that the client prominently displays the part of detail data.

Further optionally, the data preprocessing module 705 is further configured to: calculating at least one diagnosis proposal aiming at the second user according to the current symptom information of the second user and the corresponding health data of the second user, and sending the at least one diagnosis proposal to the client for display; wherein the at least one visit proposal comprises: at least one of a consultation advice, an examination item advice, a medication intake advice, and a diagnosis advice associated with the second user.

Further optionally, the intent estimation module 702 is specifically configured to, when estimating the query intent of the first user according to the data acquisition request: acquiring identity information of the first user carried by a data acquisition request; and estimating the query intention of the first user according to the identity information of the first user.

Further optionally, the intent estimating module 702 is specifically configured to, when estimating the query intent of the first user according to the identity information of the first user: if the first user is identified as a medical subsidy processing user according to the identity information of the first user, estimating that the first user requests to inquire the target disease type meeting the medical subsidy condition; the data obtaining module 703 is specifically configured to, when obtaining, from the health data corresponding to the second user, health data adapted to the query intention of the first user: and acquiring diagnosis and treatment records and diagnosis and treatment expense data corresponding to the target disease types meeting the medical subsidy condition from the health data of the second user.

Further optionally, the intent estimating module 702 is specifically configured to, when estimating the query intent of the first user according to the identity information of the first user: if the first user is identified as a business insurance processing user according to the identity information of the first user, estimating that the first user requests to inquire health data associated with business insurance; the data obtaining module 703 is specifically configured to, when obtaining, from the health data corresponding to the second user, health data adapted to the query intention of the first user: sending an authorization request to the second user according to the query intention of the first user; and if the authorization notification message returned by the second user according to the authorization request is received, acquiring health data associated with business insurance from the health data of the second user.

Further optionally, the health data associated with the business insurance includes: the health data of the second user when the second user applies for insurance claims, the health data of the second user when the second user continues applying for insurance claims, or the health data of the second user when the second user applies for insurance claims.

Further optionally, the intent estimating module 702 is specifically configured to, when estimating the query intent of the first user according to the identity information of the first user: if the first user is identified as a disease control user with a set region range according to the identity information of the first user, estimating that the first user requests to inquire the disease distribution characteristics in the set region range; the data obtaining module 703 is specifically configured to, when obtaining, from the health data corresponding to the second user, health data adapted to the query intention of the first user: medical data of the second user in a plurality of medical institutions belonging to the set region range is obtained, and disease distribution characteristics in the set region range are calculated according to the medical data.

Further optionally, the disease distribution profile comprises at least one of: frequency of visit for each disease, period of visit for each disease, medical institution for each disease, age of onset for each disease.

Further optionally, the data acquisition module 703 is further configured to: calculating a target disease with a frequency of visits greater than a set frequency threshold according to the disease distribution characteristics; the sending module 704 is further configured to: and outputting an early warning message aiming at the target disease to the client.

Further optionally, the data obtaining module 703 is configured to obtain, from the health data corresponding to the second user, health data adapted to the query intention of the first user, where the health data is specifically used as the target health data: determining the query authority of the first user according to the identity information of the first user; determining health data adapted to the query authority of the first user from the health data corresponding to the second user; and acquiring the health data matched with the query intention of the first user from the health data matched with the query authority of the first user as target health data.

Further optionally, the health data corresponding to the second user includes: medical data generated by the second user when medical institutions belonging to different regions perform diagnosis and treatment; and/or medical data generated by the second user when a department affiliated with a different medical institution makes a diagnosis.

FIG. 8 is a schematic diagram of a client according to an exemplary embodiment of the present application, which is applicable to the data processing system according to the foregoing embodiment. As shown in fig. 8, the client includes: memory 801, processor 802, and communication component 803.

Memory 801, for storing computer programs, may be configured to store various other data to support operations on the server. Examples of such data include instructions for any application or method operating on a server, contact data, phonebook data, messages, pictures, video, and the like.

A processor 802 coupled to the memory 801 for executing computer programs in the memory 801 for: responding to the operation of inquiring the health data of the second user by the first user, and sending a corresponding data acquisition request to the server through the communication component 803; receiving, by the communication component 803, target health data of the second user returned by the server according to the data acquisition request; and displaying the target health data, wherein the target health data is matched with the estimated query intention of the first user by the server.

Further optionally, the processor 802 is further configured to: displaying at least one health description tag of the second user in a tag display area for selection by the first user; acquiring a target health description tag selected by the first user from the at least one health description tag; and sending the target health description tag to the server so that the server corrects the query intention of the first user according to the target health description tag.

Further optionally, when the processor 802 presents the target health data, the method specifically is used for: displaying at least one disease group and corresponding visit times contained in the target health data in a medical history overview area; in response to a selection operation for a target disease group of the at least one disease group, at least one disease type and a corresponding number of visits under the target disease group are displayed.

Further optionally, the processor 802 is further configured to: determining the first user selected target disease type in response to a selection operation for the at least one disease type; the target disease type is sent to the server, so that the server corrects the query intention of the first user according to the target disease type.

Further optionally, the processor 802 is specifically configured to, when presenting the target health data: acquiring a plurality of data categories contained in the target health data; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category; and displaying the health data corresponding to the target data category in the plurality of data categories in the medical history detail display area, and displaying the view icons corresponding to the plurality of data categories for the first user to switch the data categories.

Further optionally, the processor 802 is specifically configured to, when presenting the health data corresponding to the target data category in the plurality of data categories: acquiring a data category which is indicated by the server and is matched with the historical viewing behavior of the first user, and taking the data category as the target data category; and preferentially displaying the health data corresponding to the target data category in the medical history detail display area.

Further optionally, the processor 802 is specifically configured to, when presenting the target health data: acquiring at least one medical history record contained in the target health data; and displaying the at least one medical history record in a time-axis display form in the medical history detail display area according to the corresponding treatment time sequence of the at least one medical history record.

Further optionally, when the at least one medical history record is presented in a presentation form of a time axis, the processor 802 is further configured to: the medical history records with the degree of association with the query intent of the first user greater than or equal to a set threshold are highlighted, and the medical history records with the degree of association with the query intent of the first user less than the set threshold are folded.

Further optionally, for any of the at least one medical history, the processor 802 is further configured to: displaying a detail view icon corresponding to the medical history record in the medical history detail display area; responding to the triggering operation aiming at the detail checking icon, and displaying the detail page of the medical history record; and displaying the detail data corresponding to the medical history record in the form of text, pictures, tables and/or charts on the detail page of the medical history record.

Further optionally, the processor 802 is further configured to: acquiring current sign information of the second user; and highlighting part of detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history record in the detail page of the medical history record.

Further optionally, the processor 802 is further configured to: displaying a screening icon in the query setting area for the first user to input screening conditions of the health data; and/or displaying an optional health data query time period in the query setting area for selection by the first user; and/or displaying a query time custom control in the query setting area for the first user to customize a query time period of the health data; and/or displaying a search component in the query setup area for the first user to enter search keywords for the health data.

Further optionally, the processor 802 is further configured to: at least one of a consultation suggestion, an examination item suggestion, a medication intake suggestion and a diagnosis suggestion, which are associated with the second user and transmitted by the server, is displayed in a suggestion display area.

Further, as shown in fig. 8, the client may also include a display 804, a power component 805, and other components such as an audio component 806. Only some of the components are schematically shown in fig. 8, which does not mean that the client only comprises the components shown in fig. 8.

Accordingly, the present application also provides a computer readable storage medium storing a computer program, where the computer program is executed to implement the steps executable by the client in the above method embodiments.

FIG. 9 is a schematic diagram of a server according to an exemplary embodiment of the present application, which is suitable for use in the data processing system according to the foregoing embodiment. As shown in fig. 9, the server includes: memory 901, processor 902, and communications component 903.

Memory 901 for storing a computer program and may be configured to store various other data to support operations on a server. Examples of such data include instructions for any application or method operating on a server, contact data, phonebook data, messages, pictures, video, and the like.

A processor 902 coupled to the memory 901 for executing a computer program in the memory 901 for: receiving a data acquisition request through the communication component 903; the data acquisition request is sent by the server when the first user inquires the health data of the second user; estimating the query intention of the first user according to the data acquisition request; acquiring health data matched with the query intention of the first user from the health data corresponding to the second user as target health data; the target health data is sent to the server for presentation via the communication component 903.

Further optionally, the processor 902 is specifically configured to, when predicting the query intention of the first user according to the data acquisition request: acquiring first intention description data associated with the first user and/or second intention description data associated with the second user according to the data acquisition request; estimating the query intention of the first user according to the first intention description data and/or the second intention description data.

Further optionally, the processor 902 is specifically configured to, when acquiring the first intention description data associated with the first user according to the data acquisition request: extracting an identification of the first user from the data acquisition request; acquiring at least one of attribute information, historical query behavior and preset query conditions of the first user according to the identification of the first user; wherein the attribute information includes: at least one of affiliated medical institutions, affiliated departments, good areas, indications, historic diagnosis and treatment records.

Further optionally, the processor 902 is specifically configured to, when acquiring the second intention description data associated with the second user according to the data acquisition request: extracting an identification of the second user from the data acquisition request; and acquiring at least one of a historical visit department, a historical visit disease, a distribution characteristic of health data, generation time of different health data and query frequency of different health data of the second user according to the identification of the second user.

Further optionally, the processor 902 is specifically configured to, when predicting the query intent of the first user according to the first intent description data and/or the second intent description data: estimating, based on the first intent description data and/or the second intent description data, at least one of the following query intents of the first user: inquiring health data corresponding to a first historical visit department of the second user; inquiring health data corresponding to the first historical disease of the second user; querying health data adapted to the query preferences of the first user; inquiring health data with the data volume of which the duty ratio is larger than a set proportion threshold value in the health data of the second user; querying health data generated by the second user during a specified historical period of time; and inquiring the health data with the inquiring frequency larger than the set frequency threshold value in the health data of the second user.

Further optionally, the processor 902 is specifically configured to, when predicting, according to the first intention description data and/or the second intention description data, that the first user queries the health data corresponding to the first historical office of medical care of the second user: acquiring a department affiliated by the first user from the first intention description data as a current consultation department of the second user; acquiring a department corresponding to at least one history visit record of the second user from the second intention description data as at least one history visit department of the second user; respectively calculating the similarity of the current visit department and the at least one historical visit department; from the at least one historical visit department, a historical visit department with a similarity to the current visit department greater than a set similarity threshold is selected as the first historical visit department.

Further optionally, the processor 902 is specifically configured to, when calculating the similarity between the current office and the at least one historical office, respectively: obtaining a code vector corresponding to the current department of medical treatment according to the diagnosis code of the disease type contained in the current department of medical treatment and tf-idf corresponding to the diagnosis code; acquiring the respective corresponding coding vector of the at least one historical visit department; and respectively calculating the similarity of the coding vector corresponding to the current visit department and the coding vector corresponding to each of the at least one historical visit department to obtain the similarity of the current visit department and the at least one historical visit department.

Further optionally, the processor 902 is further configured to: acquiring a target health description tag sent by the server; the target health description tag is selected by the first user from at least one health description tag of the second user; and correcting the query intention of the first user according to the target health description tag.

Further optionally, the processor 902 is further configured to: identifying health features of the second user based on the health data of the second user; the medical health comprises: one or more of a visit behavior feature, a treatment means feature, a physical state feature, a life habit feature; generating at least one health description tag for the second user based on the health characteristics; the at least one health description tag is sent to the server for display for the first user to select the target health description tag from.

Further optionally, the processor 902 is further configured to: obtaining a target disease type sent by the server; the target disease type is selected by the first user from at least one disease type; and correcting the query intention of the first user according to the target disease type.

Further optionally, the processor 902 is further configured to: identifying at least one disease type of the second user's historical visit based on the second user's health data; dividing the at least one disease type into at least one disease group according to the disease group to which the disease type belongs; and sending the at least one disease group and the corresponding visit times thereof, and the disease type and the corresponding visit times thereof under each disease group to the server for display.

Further optionally, the processor 902 is further configured to, before sending the target health data to the server for presentation: classifying the target health data to obtain a plurality of data classes; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category; and sending the data categories to the server for display.

Further optionally, the processor 902 is further configured to: acquiring historical viewing behaviors of the first user; analyzing the historical viewing behavior of the first user to obtain interest scores of the first user for the plurality of data categories; determining a data category with interest scores meeting a set condition from the plurality of data categories as a data category adapted to the historical viewing behavior of the first user; and sending an instruction to the server to preferentially display the data category matched with the historical viewing behavior of the first user.

Further optionally, the processor 902 is further configured to, before sending the target health data to the server for presentation: calculating the association degree of at least one medical history record and the query intention of the first user according to at least one medical history record contained in the target health data; and sending the calculated relevance degree corresponding to each at least one medical history record to the server so that the server can prominently display the medical history records with relevance degrees larger than or equal to a set threshold value.

Further optionally, the processor 902 is specifically configured to, when calculating the association degree between the at least one medical history record and the query intention of the first user, respectively: acquiring detail data corresponding to any one of the at least one medical history record; respectively calculating the relevance between the detail data and at least one type of intention description data to obtain at least one relevance calculation result; the at least one intent description data includes: the first intent description data and/or the second intent description data; and according to the weights corresponding to the at least one intention description data, carrying out weighted summation on the at least one correlation calculation result to obtain the correlation between the medical history record and the query intention of the first user.

Further optionally, the processor 902 is further configured to: acquiring current sign information of the second user; identifying partial detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history record by adopting a natural language processing algorithm aiming at any medical history record in the at least one medical history record; and sending the identification mark of the part of detail data to the server so as to enable the server to prominently display the part of detail data.

Further optionally, the processor 902 is further configured to: calculating at least one diagnosis proposal aiming at the second user according to the current symptom information of the second user and the corresponding health data of the second user, and sending the at least one diagnosis proposal to the client for display; wherein the at least one visit proposal comprises: at least one of a consultation advice, an examination item advice, a medication intake advice, and a diagnosis advice associated with the second user.

Further optionally, the processor 902 is specifically configured to, when predicting the query intention of the first user according to the data acquisition request: acquiring identity information of the first user carried by a data acquisition request; and estimating the query intention of the first user according to the identity information of the first user.

Further optionally, the processor 902 is specifically configured to, when predicting the query intention of the first user according to the identity information of the first user: if the first user is identified as a medical subsidy processing user according to the identity information of the first user, estimating that the first user requests to inquire the target disease type meeting the medical subsidy condition; the processor 902 is specifically configured to, when acquiring health data adapted to the query intention of the first user from the health data corresponding to the second user: and acquiring diagnosis and treatment records and diagnosis and treatment expense data corresponding to the target disease types meeting the medical subsidy condition from the health data of the second user.

Further optionally, the processor 902 is specifically configured to, when predicting the query intention of the first user according to the identity information of the first user: if the first user is identified as a business insurance processing user according to the identity information of the first user, estimating that the first user requests to inquire health data associated with business insurance; the processor 902 is specifically configured to, when acquiring health data adapted to the query intention of the first user from the health data corresponding to the second user: sending an authorization request to the second user according to the query intention of the first user; and if the authorization notification message returned by the second user according to the authorization request is received, acquiring health data associated with business insurance from the health data of the second user.

Further optionally, the processor 902 is specifically configured to, when predicting the query intention of the first user according to the identity information of the first user: if the first user is identified as a disease control user with a set region range according to the identity information of the first user, estimating that the first user requests to inquire the disease distribution characteristics in the set region range; the processor 902 is specifically configured to, when acquiring health data adapted to the query intention of the first user from the health data corresponding to the second user: medical data of the second user in a plurality of medical institutions belonging to the set region range is obtained, and disease distribution characteristics in the set region range are calculated according to the medical data.

Further optionally, the processor 902 is further configured to: according to the disease distribution characteristics, a target disease with a frequency of visit greater than a set frequency threshold is calculated, and an early warning message for the target disease is output to the client through the communication component 903.

Further optionally, the processor 902 obtains, from the health data corresponding to the second user, health data adapted to the query intention of the first user, where the health data is used as target health data, specifically: determining the query authority of the first user according to the identity information of the first user; determining health data adapted to the query authority of the first user from the health data corresponding to the second user; and acquiring the health data matched with the query intention of the first user from the health data matched with the query authority of the first user as target health data.

Further, as shown in fig. 9, the server further includes: power supply assembly 904, and the like. Only some of the components are schematically shown in fig. 9, which does not mean that the server only comprises the components shown in fig. 9.

In this embodiment, when the first user initiates the operation of checking the health data of the second user, the query intention of the first user may be estimated, and the target health data adapted to the query intention of the first user in the health data of the second user may be returned to the server, so as to display the target health data through the server, thereby implementing intelligent recommendation of the health data, facilitating quick display of the health data meeting the query requirement of the first user, and improving the data query efficiency.

Accordingly, the present application also provides a computer readable storage medium storing a computer program, where the computer program is executed to implement the steps executable by the server in the above method embodiments.

The memory in fig. 8 and 9 described above may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The communication assembly of fig. 8 and 9 described above is configured to facilitate wired or wireless communication between the device in which the communication assembly is located and other devices. The device in which the communication component is located may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component may be implemented based on Near Field Communication (NFC) technology, radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

The display in fig. 8 described above includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or sliding action, but also the duration and pressure associated with the touch or sliding operation.

The power supply assembly of fig. 8 and 9 provides power to the various components of the device in which the power supply assembly is located. The power components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the devices in which the power components are located.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (trans itory media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims

1. A data processing method, suitable for a client, comprising:

responding to the operation of inquiring the health data of the second user by the first user, and sending a corresponding data acquisition request to a server;

receiving target health data of the second user returned by the server according to the data acquisition request;

displaying the target health data, wherein the target health data is adapted to the estimated query intention of the first user by the server;

Wherein displaying the target health data comprises: displaying at least one disease group and corresponding visit times contained in the target health data in a medical history overview area; responsive to a selection operation for a target disease group of the at least one disease group, displaying at least one disease type and a corresponding number of visits under the target disease group;

Wherein the method further comprises: determining the first user selected target disease type in response to a selection operation for the at least one disease type; and sending the target disease type to the server so that the server corrects the query intention of the first user according to the target disease type.

2. The method as recited in claim 1, further comprising:

displaying at least one health description tag of the second user in a tag display area for selection by the first user;

acquiring a target health description tag selected by the first user from the at least one health description tag;

and sending the target health description tag to the server so that the server corrects the query intention of the first user according to the target health description tag.

3. The method of claim 1, wherein presenting the target health data comprises:

Acquiring a plurality of data categories contained in the target health data; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category;

and displaying the health data corresponding to the target data category in the plurality of data categories in the medical history detail display area, and displaying the view icons corresponding to the plurality of data categories for the first user to switch the data categories.

4. The method of claim 3, wherein presenting the health data corresponding to the target data category of the plurality of data categories comprises:

Acquiring a data category which is indicated by the server and is matched with the historical viewing behavior of the first user, and taking the data category as the target data category;

And preferentially displaying the health data corresponding to the target data category in the medical history detail display area.

5. The method of any one of claims 1-4, wherein displaying the target health data comprises:

acquiring at least one medical history record contained in the target health data;

And displaying the at least one medical history record in a time-axis display form in a medical history detail display area according to the corresponding treatment time sequence of the at least one medical history record.

6. The method of claim 5, wherein the at least one medical history record is presented in a presentation form of a timeline, further comprising:

Highlighting medical history records that exhibit a degree of association with the first user's query intent that is greater than or equal to a set threshold, and folding medical history records that exhibit a degree of association with the first user's query intent that is less than the set threshold.

7. The method of claim 5, wherein for any of the at least one medical history records, further comprising:

displaying a detail view icon corresponding to the medical history record in the medical history detail display area;

Responding to the triggering operation aiming at the detail viewing icon, and displaying the detail page of the medical history record;

and displaying the detail data corresponding to the medical history record in the form of text, picture, table and/or chart on the detail page of the medical history record.

8. The method as recited in claim 7, further comprising:

Acquiring current sign information of the second user;

And highlighting part of detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history record in the detail page of the medical history record.

9. The method of any one of claims 1-4, further comprising:

displaying a screening icon in a query setting area so that the first user can input screening conditions of the health data; and/or the number of the groups of groups,

Displaying an optional health data query time period in the query setting area for the first user to select; and/or the number of the groups of groups,

Displaying a query time custom control in the query setting area so as to enable the first user to customize a query time period of the health data; and/or the number of the groups of groups,

And displaying a search component in the query setting area for the first user to input search keywords aiming at the health data.

10. The method of any one of claims 1-4, further comprising:

And displaying at least one of inquiry advice, examination item advice, medicine taking advice and diagnosis advice which are sent by the server and are associated with the second user in the advice display area.

11. A data processing method, suitable for a server, comprising:

receiving a data acquisition request, wherein the data acquisition request is sent by a client when a first user inquires health data of a second user;

Estimating the query intention of the first user according to the data acquisition request;

Acquiring health data matched with the query intention of the first user from the health data corresponding to the second user as target health data;

the target health data are sent to the client for display;

Wherein, according to the data acquisition request, estimating the query intention of the first user includes: acquiring first intention description data associated with the first user and/or second intention description data associated with the second user according to the data acquisition request; estimating the query intention of the first user according to the first intention description data and/or the second intention description data;

Wherein estimating the query intent of the first user based on the first intent description data and/or the second intent description data comprises: acquiring a department affiliated by the first user from the first intention description data as a current consultation department of the second user; acquiring a department corresponding to at least one history visit record of the second user from the second intention description data as at least one history visit department of the second user; respectively calculating the similarity of the current visit department and the at least one historical visit department; selecting a historical visit department with similarity with the current visit department being greater than a set similarity threshold from the at least one historical visit department as a first historical visit department of the second user; estimating the query intention of the first user as the health data corresponding to the first historical visit department.

12. The method of claim 11, wherein retrieving the first intent description data associated with the first user in accordance with the data retrieval request comprises:

extracting an identification of the first user from the data acquisition request;

Acquiring at least one of attribute information, historical query behavior and preset query conditions of the first user according to the identification of the first user;

wherein the attribute information includes: at least one of affiliated medical institutions, affiliated departments, good areas, indications, historic diagnosis and treatment records.

13. The method of claim 11, wherein retrieving second intent description data associated with the second user in accordance with the data retrieval request comprises:

Extracting an identification of the second user from the data acquisition request;

and acquiring at least one of a historical visit department, a historical visit disease, distribution characteristics of health data, generation time of different health data and query frequency of different health data of the second user according to the identification of the second user.

14. The method of claim 11, wherein estimating the query intent of the first user based on the first intent description data and/or the second intent description data, further comprises:

Estimating at least one of the following query intents of the first user according to the first intention description data and/or the second intention description data:

inquiring health data corresponding to the first historical disease of the second user;

querying health data adapted to the query preferences of the first user;

Inquiring health data with the data volume of which the duty ratio is larger than a set proportion threshold value in the health data of the second user;

Querying health data generated by the second user during a specified historical period of time;

And inquiring health data with the inquiring frequency larger than a set frequency threshold value in the health data of the second user.

15. The method of claim 11, wherein calculating the similarity of the current visit department and the at least one historical visit department, respectively, comprises:

Obtaining a coding vector corresponding to the current medical department according to the diagnosis codes of the disease types contained in the current medical department and tf-idf corresponding to the diagnosis codes;

acquiring the respective corresponding coding vector of the at least one historical visit department;

And respectively calculating the similarity of the coding vector corresponding to the current visit department and the coding vector corresponding to each of the at least one historical visit department so as to obtain the similarity of the current visit department and the at least one historical visit department.

16. The method as recited in claim 11, further comprising:

Acquiring a target health description tag sent by the client; the target health description tag is selected by the first user from at least one health description tag of the second user; and correcting the query intention of the first user according to the target health description tag.

17. The method as recited in claim 16, further comprising:

identifying health features of the second user according to the health data of the second user; the health features of the second user include: one or more of a visit behavior feature, a treatment means feature, a physical state feature, a life habit feature;

generating at least one health description tag of the second user according to the health characteristics;

and sending the at least one health description tag to the client for display, so that the first user can select the target health description tag from the at least one health description tag.

18. The method as recited in claim 11, further comprising:

acquiring a target disease type sent by the client; the target disease type is selected by the first user from at least one disease type;

And correcting the query intention of the first user according to the target disease type.

19. The method as recited in claim 18, further comprising:

Identifying at least one disease type of the second user's historical visits based on the second user's health data;

dividing the at least one disease type into at least one disease group according to the disease group to which the disease type belongs;

And sending the at least one disease group and the corresponding times of the treatment, and the disease type and the corresponding times of the treatment under each disease group to the client for display, so that the first user can select the target disease type from the first user.

20. The method of claim 11, wherein sending the target health data to the client for presentation comprises:

classifying the target health data to obtain a plurality of data categories; the plurality of data categories includes: at least one of a comprehensive category, a disorder category, an examination category, an assay category, a treatment category, a surgical category, a drug category, and an epidemic prevention inoculation category;

And sending the health data corresponding to the data categories to the client for display.

21. The method as recited in claim 20, further comprising:

acquiring historical viewing behaviors of the first user;

analyzing the historical viewing behavior of the first user to obtain interest scores of the first user for the plurality of data categories;

determining a data category with interest scores meeting a set condition from the plurality of data categories as a data category adapted to the historical viewing behavior of the first user;

and sending an instruction for preferentially displaying the data category matched with the historical viewing behavior of the first user to the client.

22. The method of claim 20, wherein prior to sending the target health data to the client for presentation, further comprising:

Calculating the association degree of at least one medical history record and the query intention of the first user aiming at least one medical history record contained in the target health data;

And sending the calculated relevance degree corresponding to each at least one medical history record to the client so that the client highlights the medical history records with the relevance degree larger than or equal to a set threshold.

23. The method of claim 22, wherein calculating the relevance of the at least one medical history record to the first user's query intent, respectively, comprises:

acquiring detail data corresponding to any one of the at least one medical history record;

Respectively calculating the relevance between the detail data and at least one type of intention description data to obtain at least one relevance calculation result; the at least one intent description data includes: the first intent description data and/or the second intent description data;

and carrying out weighted summation on the at least one correlation calculation result according to the weight corresponding to the at least one intention description data to obtain the correlation between the medical history record and the query intention of the first user.

24. The method as recited in claim 22, further comprising:

Acquiring current sign information of the second user;

Identifying partial detail data matched with the current symptom information of the second user in the detail data corresponding to the medical history records by adopting a natural language processing algorithm aiming at any medical history record in the at least one medical history record;

and sending the identification of the part of detail data to the client so as to enable the client to highlight and display the part of detail data.

25. The method as recited in claim 24, further comprising:

Calculating at least one diagnosis proposal aiming at the second user according to the current symptom information of the second user and the health data corresponding to the second user, and sending the at least one diagnosis proposal to the client for display;

Wherein the at least one visit proposal comprises: at least one of a consultation advice, an examination item advice, a medication intake advice, and a diagnosis advice associated with the second user.

26. The method of claim 11, wherein predicting the first user's query intent based on the data acquisition request comprises:

Acquiring identity information of the first user carried by a data acquisition request;

And estimating the query intention of the first user according to the identity information of the first user.

27. The method of claim 26, wherein predicting the first user's query intent based on the first user's identity information comprises:

If the first user is identified as a medical subsidy processing user according to the identity information of the first user, estimating that the first user requests to inquire a target disease type meeting medical subsidy conditions;

Obtaining health data adapted to the query intention of the first user from the health data corresponding to the second user, including:

And acquiring diagnosis and treatment records and diagnosis and treatment expense data corresponding to the target disease types meeting the medical subsidy condition from the health data of the second user.

28. The method of claim 26, wherein predicting the first user's query intent based on the first user's identity information comprises:

if the first user is identified as a business insurance processing user according to the identity information of the first user, estimating that the first user requests to inquire health data associated with business insurance;

sending an authorization request to the second user according to the query intention of the first user;

and if the authorization notification message returned by the second user according to the authorization request is received, acquiring health data associated with business insurance from the health data of the second user.

29. The method of claim 28, wherein the health data associated with a business insurance comprises:

the health data of the second user when the second user applies for insurance claims, the health data of the second user when the second user continues applying for insurance claims, or the health data of the second user when the second user applies for insurance claims.

30. The method of claim 26, wherein predicting the first user's query intent based on the first user's identity information comprises:

If the first user is identified as a disease control user with a set region range according to the identity information of the first user, estimating that the first user requests to inquire the disease distribution characteristics in the set region range;

And acquiring medical data of the second user in a plurality of medical institutions belonging to the set regional scope, and calculating disease distribution characteristics in the set regional scope according to the medical data.

31. The method of claim 30, wherein the disease distribution profile comprises at least one of: frequency of visit for each disease, period of visit for each disease, medical institution for each disease, age of onset for each disease.

32. The method as recited in claim 30, further comprising:

Calculating target diseases with the treatment frequency greater than a set frequency threshold according to the disease distribution characteristics;

And outputting an early warning message aiming at the target disease to the client.

33. The method according to any one of claims 11-32, wherein obtaining, from the health data corresponding to the second user, health data adapted to the query intent of the first user as target health data, comprises:

Determining the query authority of the first user according to the identity information of the first user;

determining health data adapted to the query authority of the first user from the health data corresponding to the second user;

And acquiring health data adapted to the query intention of the first user from the health data adapted to the query authority of the first user, and taking the health data as the target health data.

34. The method of any one of claims 11-32, wherein the health data corresponding to the second user comprises: medical data generated by the second user when medical institutions belonging to different regions diagnose; and/or medical data generated by the second user when a department affiliated with a different medical institution makes a diagnosis.

35. A health data display device, comprising:

the request module is used for responding to the operation of inquiring the health data of the second user by the first user and sending a corresponding data acquisition request to the server;

the receiving module is used for receiving target health data of the second user returned by the server according to the data acquisition request;

the display module is used for displaying the target health data, and the target health data is matched with the query intention of the first user estimated by the server;

wherein, the show module includes: a medical history overview sub-module;

The medical history overview submodule is used for: displaying at least one disease group and corresponding visit times contained in the target health data in a medical history overview area; responsive to a selection operation for a target disease group of the at least one disease group, displaying at least one disease type and a corresponding number of visits under the target disease group; determining the first user selected target disease type in response to a selection operation for the at least one disease type;

The device also comprises a sending module; the sending module is used for sending the target disease type to the server so that the server corrects the query intention of the first user according to the target disease type.

36. A health data display device, comprising:

The receiving module is used for receiving a data acquisition request, and the data acquisition request is sent by the client when the first user inquires the health data of the second user;

The intention estimating module is used for estimating the query intention of the first user according to the data acquisition request;

the data acquisition module is used for acquiring health data matched with the query intention of the first user from the health data corresponding to the second user as target health data;

the sending module is used for sending the target health data to the client for display;

the intention estimating module is specifically configured to, when estimating the query intention of the first user according to the data acquisition request: acquiring first intention description data associated with the first user and/or second intention description data associated with the second user according to the data acquisition request; estimating the query intention of the first user according to the first intention description data and/or the second intention description data;

The intent estimation module is specifically configured to, when estimating the query intent of the first user according to the first intent description data and/or the second intent description data: acquiring a department affiliated by the first user from the first intention description data as a current consultation department of the second user; acquiring a department corresponding to at least one history visit record of the second user from the second intention description data as at least one history visit department of the second user; respectively calculating the similarity of the current visit department and the at least one historical visit department; selecting a historical visit department with similarity with the current visit department being greater than a set similarity threshold from the at least one historical visit department as a first historical visit department of the second user; estimating the query intention of the first user as the health data corresponding to the first historical visit department.

37. A client, comprising: a memory, a processor, and a communication component;

the memory is used for storing one or more computer instructions;

The processor is configured to execute the one or more computer instructions to: performing the steps of the data processing method of any of claims 1-10.

38. A server, comprising: a memory, a processor, and a communication component;

the memory is used for storing one or more computer instructions;

The processor is configured to execute the one or more computer instructions to: performing the steps of the data processing method of any of claims 11-34.

39. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed, is capable of realizing the steps in the data processing method of any one of claims 1-34.

40. A data processing system, comprising: a client and a server;

Wherein, the client is used for: according to the operation of inquiring the health data of the second user by the first user, a corresponding data acquisition request is sent to the server, target health data of the second user returned by the server is received, and the target health data is displayed; wherein displaying the target health data comprises: displaying at least one disease group and corresponding visit times contained in the target health data in a medical history overview area; responsive to a selection operation for a target disease group of the at least one disease group, displaying at least one disease type and a corresponding number of visits under the target disease group; the server is used for: when the data acquisition request is received, estimating the query intention of the first user, acquiring health data matched with the query intention of the first user from the health data corresponding to the second user, taking the health data as target health data, and sending the target health data to the client;

Wherein, the client is further configured to: determining the first user selected target disease type in response to a selection operation for the at least one disease type; and sending the target disease type to the server so that the server corrects the query intention of the first user according to the target disease type.