CN117093766A - Information recommendation method, related device and storage medium of inquiry platform - Google Patents

Information recommendation method, related device and storage medium of inquiry platform Download PDF

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Publication number
CN117093766A
CN117093766A CN202310436916.0A CN202310436916A CN117093766A CN 117093766 A CN117093766 A CN 117093766A CN 202310436916 A CN202310436916 A CN 202310436916A CN 117093766 A CN117093766 A CN 117093766A
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case
data
historical
consultation
information
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翟晓慧
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Shanghai Jiahe Health Management Co ltd
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Shanghai Jiahe Health Management Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
    • G06F21/6254—Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Theoretical Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Pathology (AREA)
  • Bioethics (AREA)
  • Computer Hardware Design (AREA)
  • Computer Security & Cryptography (AREA)
  • Software Systems (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

The embodiment of the application relates to the field of data processing, and provides an information recommendation method of a consultation platform, a related device and a storage medium, wherein the method comprises the following steps: obtaining case data of a target visit object; the case data are obtained through pre-consultation data fed back by the target consultation object; determining the similarity between the case data and the historical medical record data of the historical visit object; screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended; and generating introduction information of the case to be recommended, and displaying the introduction information to the target consultation object. According to the method, a high-efficiency data relation form is formed between the target diagnosis object and the historical diagnosis object, automatic case information recommendation of the target diagnosis object is achieved, the target diagnosis object is assisted in establishing primary cognition on the illness state, and service experience of the diagnosis object is effectively improved.

Description

Information recommendation method, related device and storage medium of inquiry platform
Technical Field
The embodiment of the application relates to the field of data processing, in particular to an information recommendation method of a consultation platform, a related device and a storage medium.
Background
In recent years, with the development of internet technology, more and more people choose to query the illness state information through the internet. However, patients lack basic knowledge of the condition, and the information queried on the internet is often not accurate enough.
In the related art, in order to provide more accurate disease information, a patient can describe own disease symptoms in an on-line inquiry platform, a doctor makes diagnosis in time according to the symptoms described by the patient through the on-line inquiry platform, and corresponding advice is provided.
However, at present, the related technology is limited by the number of online doctors, the online time, the reserved number and the like, and patients still need to wait for a certain time to receive the doctor's answering reply, so that the patients easily experience poor doctor experience due to lack of cognition on the illness state in the waiting process.
Disclosure of Invention
The embodiment of the application provides an information recommendation method, a related device and a storage medium of a consultation platform, which can realize automatic case information recommendation of a target consultation object, assist the target consultation object to establish preliminary cognition on illness conditions and effectively promote service experience of the consultation object.
In a first aspect, an embodiment of the present application provides an information recommendation method of a consultation platform, where the method includes:
Obtaining case data of a target visit object; the case data are obtained through pre-consultation data fed back by the target consultation object;
determining the similarity between the case data and the historical medical record data of the historical visit object;
screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended;
and generating introduction information of the case to be recommended, and displaying the introduction information to the target doctor-seeing object.
In one possible design, obtaining case data for a target visit subject includes:
receiving pre-consultation data of the target consultation object; the pre-consultation data are input by the target consultation object according to a pre-consultation flow; extracting the case data from the pre-consultation data.
Wherein the case data includes at least one of basic information, affected parts, affected symptoms, time of onset, frequency of onset, status of medical consultation, and patient complaint information of the target consultation subject.
In one possible design, determining a similarity between the case data and historical medical record data of the historical visit subject includes:
extracting first case feature information belonging to a first priority from the case data according to the preset priority; taking a history visit object with the first case characteristic information in the history medical record data as a first classification group; extracting second case feature information belonging to a second priority from the case data; the second priority is lower than the first priority; extracting third case feature information belonging to the second priority from the historical medical record data of each historical visit object in the first classification group; and calculating the similarity between the second case feature information and the third case feature information.
The preset priority at least comprises: the affected part belongs to one of departments, patient complaints, medicine taking history, operation history, age groups and sexes.
In one possible design, screening the historical medical record data of the historical visit object with similarity meeting the preset condition to obtain the case to be recommended includes:
selecting a historical visit object, of which the similarity between the second case characteristic information and the third case characteristic information reaches a set threshold, from the first classification group; and taking the historical medical record data of the historical visit object with the similarity reaching the set threshold value as the case to be recommended.
In one possible design, after extracting the second case feature information belonging to the second priority from the case data, the method further includes:
if the historical visit object in the first classification group does not have the third case feature information belonging to the second priority, the case feature information of the next priority is adopted, and the similarity between the case data and the historical medical record data of each historical visit object in the first classification group is calculated.
In one possible design, generating introduction information of the case to be recommended includes:
Acquiring text information and/or image information associated with the case to be recommended; and/or desensitizing the sensitive information in the case to be recommended to obtain the inquiry introduction information for introducing the inquiry process.
In one possible design, the method further comprises: judging whether historical consultation data of the target consultation object is stored in a consultation platform; if the historical inquiry data are stored in the inquiry platform, judging whether the historical inquiry data have first case characteristic information belonging to a first priority in the case data or not; and if the historical inquiry data has the first case characteristic information, combining the historical inquiry data to determine the similarity between the case data and the historical medical record data of the historical visit object.
In a second aspect, an embodiment of the present application provides an information recommendation apparatus for a query platform, which has a function of implementing an information recommendation method corresponding to the query platform provided in the first aspect. The functions may be implemented by executing corresponding software. The software includes one or more modules corresponding to the functions described above, which may be software.
In one embodiment, the apparatus comprises:
the input-output module is configured to acquire case data of a target treatment object; the case data are obtained through pre-consultation data fed back by the target consultation object;
a processing module configured to determine a similarity between the case data and historical medical record data of the historical visit object; screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended; and generating introduction information of the case to be recommended, and displaying the introduction information to the target consultation object.
In one possible design, the processing module, when acquiring case data of the target visit object, is configured to:
receiving pre-consultation data of the target consultation object; the pre-consultation data are input by the target consultation object according to a pre-consultation flow;
extracting the case data from the pre-consultation data;
wherein the case data includes at least one of basic information, affected parts, affected symptoms, time of onset, frequency of onset, status of medical consultation, and patient complaint information of the target consultation subject.
In one possible design, the processing module, when determining the similarity between the case data and the historic medical record data of the historic visit object, is configured to:
Extracting first case feature information belonging to a first priority from the case data according to the preset priority;
taking a history visit object with the first case characteristic information in the history medical record data as a first classification group;
extracting second case feature information belonging to a second priority from the case data; the second priority is lower than the first priority;
extracting third case feature information belonging to the second priority from the historical medical record data of each historical visit object in the first classification group;
calculating the similarity between the second case feature information and the third case feature information;
the preset priority at least comprises: the affected part belongs to one of departments, patient complaints, medicine taking history, operation history, age groups and sexes.
In one possible design, the processing module is configured to, when obtaining the case to be recommended, screen the historical medical record data of the historical visit object with the similarity meeting the preset condition:
selecting a historical visit object, of which the similarity between the second case characteristic information and the third case characteristic information reaches a set threshold, from the first classification group;
And taking the historical medical record data of the historical visit object with the similarity reaching the set threshold value as the case to be recommended.
In one possible design, the processing module is further configured to:
after extracting the second case feature information belonging to the second priority from the case data, if the history visit object in the first classification group does not have the third case feature information belonging to the second priority
And calculating the similarity between the case data and the historical medical record data of each historical visit object in the first classification group by adopting the case characteristic information of the next priority.
In one possible design, the processing module, when generating the introduction information of the case to be recommended, is configured to:
acquiring text information and/or image information associated with the case to be recommended; and/or the number of the groups of groups,
and desensitizing the sensitive information in the case to be recommended to obtain inquiry introduction information for introducing an inquiry process.
In one possible design, the processing module is further configured to:
judging whether historical consultation data of the target consultation object is stored in a consultation platform;
if the historical inquiry data are stored in the inquiry platform, judging whether the historical inquiry data have first case characteristic information belonging to a first priority in the case data or not;
And if the historical inquiry data has the first case characteristic information, combining the historical inquiry data to determine the similarity between the case data and the historical medical record data of the historical visit object.
In a third aspect, an embodiment of the present application provides a computing device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements the information recommendation method of the inquiry platform described in the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to perform the information recommendation method of the inquiry platform described in the first aspect.
Compared with the related art, in the embodiment of the application, the case data of the target visit object is acquired first, and the case data is obtained through the pre-inquiry data fed back by the target visit object. Then, determining the similarity between the case data and the historical medical record data of the historical diagnosis object, and screening the historical medical record data of the historical diagnosis object, wherein the similarity meets the preset condition, so as to obtain the case to be recommended. Through the screening, an efficient data relationship form can be formed between the target treatment object and the historical treatment object, and a data basis is provided for the follow-up promotion of the treatment experience of the treatment object. Finally, the introduction information of the case to be recommended is generated, and the introduction information is displayed to the target doctor-seeing object, so that the automatic case information recommendation of the target doctor-seeing object is realized, the target doctor-seeing object is assisted to establish the primary cognition of the illness state, and the service experience of the doctor-seeing object is effectively improved.
Drawings
The objects, features and advantages of embodiments of the present application will become readily apparent from the detailed description of the embodiments of the present application read with reference to the accompanying drawings. Wherein:
fig. 1 is a schematic diagram of an information recommendation system of a consultation platform according to an embodiment of the present application;
fig. 2 is a flow chart of an information recommendation method of a consultation platform according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating another information recommendation method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an information recommendation device of a consultation platform according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a computing device according to an embodiment of the application;
FIG. 6 is a schematic diagram of a mobile phone according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
The terms first, second and the like in the description and in the claims of embodiments of the application and in the above-described figures are used for distinguishing between similar objects (e.g. first and second features each shown as a different feature, and other similar) and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those listed or explicitly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules that may not be listed or inherent to such process, method, article, or apparatus, and the partitioning of such modules by embodiments of the application may include only one logical partitioning, and may be implemented in additional partitions, such as a plurality of modules may be combined or integrated into another system, or some features may be omitted or not implemented. In addition, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling between modules via interfaces, and the communication connection may be in electrical or other similar forms, which are not limited in this embodiment. The modules or sub-modules described as separate components may or may not be physically separate, may or may not be physical modules, or may be distributed in a plurality of circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purposes of the embodiment of the present application.
The embodiment of the application provides an information recommendation method of a consultation platform, which can be applied to an asset management scene and relates to at least one service device. For example, a service device comprises information recommending means of a consultation platform for performing steps of different phases in the information recommendation of the consultation platform. For example, the information recommending device of the inquiry platform is used for acquiring case data of the target patient, screening the case to be recommended according to the similarity between the case data and the history medical record data of the history patient, introducing the case to the target patient, and facilitating the target patient to establish primary cognition on the illness state. The information recommending device of the inquiry platform can be used for acquiring case data of the target diagnosis object, screening a case to be recommended according to the similarity between the case data and the historical medical record data of the historical diagnosis object, introducing the case to be recommended to an application program of the target diagnosis object, or installing the case data of the acquired target diagnosis object, screening the case to be recommended according to the similarity between the case data and the historical medical record data of the historical diagnosis object, and introducing the case to be recommended to terminal equipment or server equipment of the application program of the target diagnosis object.
In the on-line inquiry scene in the related technology, in order to provide more accurate disease information, a patient can describe own disease symptoms in an on-line inquiry platform, a doctor makes diagnosis in time according to the symptoms described by the patient through the on-line inquiry platform, and corresponding advice is provided. Through the on-line consultation platform, communication between doctors and patients is not limited to fixed scenes such as medical institutions, so that time and space limitations are broken through, and the patients can visit more conveniently. Taking the appointment diagnosis scene as an example, the related technology still needs the patient to wait for a certain time to receive the diagnosis reply of the doctor, so that the patient is easy to generate anxiety emotion due to lack of cognition on the illness state in the waiting process, and the diagnosis experience is poor.
Compared with the doctor-seeing mode requiring invalid waiting of the doctor for the patient in the related technology, the method and the device can provide introduction information of similar cases for the doctor based on the pre-inquiring data, form a high-efficiency data relation form between the target-seeing object and the history-seeing object, realize automatic case information recommendation of the target-seeing object, assist the target-seeing object to establish primary cognition of the illness state, and effectively promote service experience of the seeing object.
In some embodiments, the information recommending device of the inquiry platform is one or more. The information recommendation device of the plurality of inquiry platforms can be distributed or centralized, and referring to fig. 1, the information recommendation method of the inquiry platform provided by the embodiment of the application can be realized based on the information recommendation system of the inquiry platform shown in fig. 1. In fig. 1, the information recommending apparatuses a, B, C of the consultation platform are respectively used for processing the historical medical service data (such as the historical medical record data and the historical consultation data hereinafter) stored in the data center, for example, the information recommending apparatuses a, B, C of the consultation platform are respectively used for processing the medical service data of the department a, the medical service data of the department B and the medical service data of the department C according to the division of the consultation departments. In other examples, the division may be based on patient attributes such as patient age, number of patient visits (initial, re-diagnosis), patient visit (e.g., telephone, or graphics), etc. Other divisions are not expanded here. In practical application, the information recommendation device of one inquiry platform can also be used for processing medical service data of a plurality of departments, and the application is not limited. The information recommending device of the inquiry platform can be an application program, a server or terminal equipment.
It should be noted that, the server according to the embodiment of the present application may be an independent physical server, or may be a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and an artificial intelligence platform.
The terminal device according to the embodiment of the present application may be a device that provides voice and/or data connectivity to a user, a handheld device with a wireless connection function, or other processing device connected to a wireless modem. Such as mobile telephones (or "cellular" telephones) and computers with mobile terminals, which can be portable, pocket, hand-held, computer-built-in or car-mounted mobile devices, for example, which exchange voice and/or data with radio access networks. For example, personal communication services (English full name: personal Communication Service, english short name: PCS) telephones, cordless telephones, session Initiation Protocol (SIP) phones, wireless local loop (Wireless Local Loop, english short name: WLL) stations, personal digital assistants (English full name: personal Digital Assistant, english short name: PDA) and the like.
Referring to fig. 2, fig. 2 is a flowchart of an information recommendation method of a query platform according to an embodiment of the present application. The method can be applied to an information recommendation device of a consultation platform in an asset management scene, the information recommendation device of the consultation platform is used for executing the method, case data of a target consultation object are obtained, further, cases to be recommended are screened according to similarity between the case data and history medical record data of the history consultation object, and introduced to the target consultation object, so that the target consultation object can conveniently establish preliminary cognition on illness conditions. The information recommendation method of the inquiry platform comprises the following steps:
step S210, obtaining case data of the target visit object.
In the embodiment of the application, the target diagnosis target refers to a user needing a consultation service. Such as a patient accessing an online consultation platform. Alternatively, other forms of consultants are possible and are not limited in this regard.
The case data is mainly used for describing the disease information required by the target patient, such as basic information of the target patient, diseased parts, diseased symptoms, disease time, disease frequency, medical inquiry state (whether to be a review), patient complaint information, medicine taking history and operation history. Of course, the case data may include one or more of the above information, and may include other information, which is not limited herein. It can be understood that in practical application, the type of the case data can be set in a self-defined manner according to the practical application requirement.
In an alternative design, case data may be obtained from pre-consultation data fed back by the target visit subject. Illustratively, pre-consultation data of the target consultation object is received, wherein the pre-consultation data is input by the target consultation object according to a pre-consultation flow and is used for guiding the target consultation object to feed back part or all of disease information required to be known when a doctor takes a consultation.
It will be appreciated that the pre-consultation procedure may be dynamically set according to feedback from the target consultation subject, as well as the actual consultation requirements. For example, the pre-consultation procedure may be set to a dialogue form, and the dialogue content is determined by the type of the consultation data to be acquired and the semantic recognition result of the feedback information of the target consultation object. After the pre-consultation data is acquired, case data is extracted from the pre-consultation data. Among them, case data includes, but is not limited to: at least one of basic information of a target visit subject, a diseased part, a diseased symptom, a disease time, a disease frequency, a visit status, patient complaint information, a medicine taking history, and a surgical history. Specifically, the step of extracting case data may be implemented such that pre-consultation data is input into a feature extraction model, and multi-dimensional case features (i.e., case data) of the target consultation object are output from the feature extraction model. The feature extraction model includes, but is not limited to, a semantic recognition model.
Optionally, before S210, it may also be determined whether the time of the target patient is in the current period. For example, whether the target visit subject is a current day visit. If the time of the target visit object is in the current period, the waiting time of the target visit object is short, and in this case, in order to save the computing resources, the execution of the subsequent steps may be suspended, and no similar cases are recommended to the target visit object. Further, if the waiting time of the target visit object exceeds the set threshold and the reserved visit time of the target visit object is exceeded, continuing to execute the subsequent steps, and recommending similar cases to the target visit object again.
Referring to the information recommendation flow shown in fig. 3, it is also determined whether or not the appointment is a visit, before S210. If it is determined that the appointment is to be made, the waiting time is long, and in this case, the subsequent steps S210 to S240 may be executed. If the appointment is not determined, the waiting time is short, in which case the execution of the subsequent steps may be stopped, and the introduction information of the case to be recommended (i.e., the similar case) is not presented to the target appointment object.
Step S220, determining a similarity between the case data and the history medical record data of the history visit object.
Step S230, screening the historical medical record data of the historical visit object with the similarity meeting the preset condition to obtain the case to be recommended.
In the embodiment of the present application, the history visit object includes a user who has acquired a visit service in the consultation platform, or a user who registers and authorizes to enter history medical record information in the consultation platform, or other forms of users, which are not described one by one herein.
The history medical record data of the history visit object is mainly used for recording the illness state information of the history visit object, and comprises but is not limited to: one or more of basic information of a subject who is historically treated, a diseased part, a diseased symptom, a disease time, a disease frequency, a medical consultation state (whether or not to be a review), patient complaint information, a medicine taking history, and a surgical history. Optionally, the inquiry platform may obtain the historical medical record data of the historical visit object from the cloud platform under the authorized condition. Alternatively, the inquiry platform may acquire the history medical record data of the history visit object stored in the inquiry platform.
Specifically, an alternative implementation manner of step S220 is to extract the first case feature information belonging to the first priority from the case data according to the preset priority. The preset priority comprises the following steps: at least one of department to which the affected part belongs (or department to which abnormality detection result belongs), patient complaints, medical administration history, operation history, age group, sex. For example, from high to low by priority may be: the department to which the affected part belongs (or the department to which the abnormality detection result belongs), the patient's complaint, the history of taking the medicine, the history of operation, the age group, the sex. Further, the history visit object having the first case feature information in the history medical record data is set as a first classification group. Therefore, the classification groups needing to be subjected to comparison analysis can be primarily screened out through the first priority, so that the similarity calculation time is shortened, and the similarity calculation efficiency is improved.
For ease of description, for example, assume that the first priority is the highest priority. Let it be assumed that the first priority is the department to which the affected part belongs. Then, based on the above assumption, if the affected part is liver based on the case data extraction, the department to which the subject belongs is hepatobiliary surgery (i.e., the first case feature information). Furthermore, all of the history medical record data belonging to the history visit subject of the hepatobiliary surgery visit are used as the first classification group.
Of course, the same affected part may also correspond to a plurality of departments. For example, the affected part is the head (which is manifested as dizziness), and the department is neurology department, otorhinolaryngology department. In this case, all the historic medical record data belonging to the department of neurology and otorhinolaryngology are treated as the first classification group based on the assumption. Optionally, a plurality of first sub-classification groups, such as a first sub-classification group belonging to neurology and a second sub-classification group belonging to otorhinolaryngology, may also be provided, and the subsequent steps may be performed based on the two sub-classification groups, respectively.
Further, in step S220, after the first classification group is acquired, second case feature information belonging to a second priority is extracted from the case data. The second priority is lower than the first priority. The second priority may be a single pathological feature dimension or may be composed of a plurality of pathological feature dimensions in parallel. Then, third case feature information belonging to the second priority is extracted from the history medical record data of each history visit object in the first classification group, and the similarity between the second case feature information and the third case feature information is calculated. In practical applications, optionally, the historical case data in the first classification group may be obtained from a corresponding department database.
Continuing with the example above, assume that the first priority is the highest priority. Let it be assumed that the first priority is the department to which the affected part belongs. Assuming that the affected part is the liver, the department is hepatobiliary surgery (i.e., characteristic information of the first case). The second priority is assumed to be the next highest priority. The second priority is assumed to be patient complaint symptoms.
Then, based on the above assumption, the first keyword (i.e., the second case feature information) belonging to the patient complaint symptoms is extracted from the case data by the semantic recognition technique. For example, the first keyword may be liver pain, fatty liver. Further, a second keyword (i.e., third case feature information) belonging to the patient complaint symptoms is extracted from the history medical record data of each history visit object in the first classification group. Finally, based on the first keywords of liver pain and fatty liver in the case data, whether descriptions similar to the first keywords (namely, second keywords) exist in the history medical record data of each history visit object or not is judged in sequence. And calculating the similarity between the first keyword and the second keyword of each historical diagnosis object (namely, the similarity between the case data and the historical medical record data of each historical diagnosis object) according to the judging result (such as the position of the keyword, the illness state degree indicated by the keyword, the occurrence frequency of the same keyword, the coincidence degree of the keywords and the like).
It should be noted that, because the individual differences in the actual application are large, so that there are many information differences in the history medical record data, in order to improve the accuracy of the similarity calculation, after the second case feature information belonging to the second priority is extracted from the case data in step S220, if the history medical record object in the first classification group does not have the third case feature information belonging to the second priority, the similarity between the case data and the history medical record data of each history medical record object in the first classification group may also be calculated by using the case feature information of the next priority. For example, based on the description of the above example, if the historic visit subjects in the first classification group do not have the third case feature information belonging to the complaint symptoms of the patient, the pathological feature information of one or more dimensions of the medication history, the operation history, the age group, the sex, and the like can be extracted to calculate the similarity between the case data and the historic medical record data of each historic visit subject in the first classification group.
Based on the similarity determination method described above, an alternative implementation manner in step S230 is to select a historical visit object, in which the similarity between the second case feature information and the third case feature information reaches the set threshold, from the first classification group. Further, the history medical record data of the history visit object with the similarity reaching the set threshold value is used as the case to be recommended.
Continuing with the example above, assume that the first priority is the highest priority. Let it be assumed that the first priority is the department to which the affected part belongs. Assuming that the affected part is the liver, the department is hepatobiliary surgery (i.e., characteristic information of the first case). The second priority is assumed to be the next highest priority. The second priority is assumed to be patient complaint symptoms.
Then, based on the above assumption, the history visit object whose similarity between the first keyword and the second keyword of each history visit object reaches 70% (i.e., the threshold value is set) is selected from the first classification group. Further, the history medical record data of the history visit object with the similarity reaching 70% is used as the case to be recommended.
Of course, the threshold value may be set in practical application according to the actual screening requirement, which is only an example and not limited.
In addition to the static threshold set in the above example, further optionally, in an embodiment of the present application, the patient waiting time of the target patient may also be monitored. The monitoring function can be realized through a monitoring service in a consultation platform, can also be realized through a cloud native custom detection service, and can also be realized through other technologies. Furthermore, according to the monitored diagnosis waiting time of the target diagnosis target, the set threshold is dynamically adjusted, so that richer case introduction information is pushed to the diagnosis target with longer waiting time, anxiety generated in the waiting time is reduced, and the diagnosis experience is improved.
Therefore, the automatic acquisition of similar cases can be realized by introducing a multi-dimensional screening mode of priority, so that an efficient data relationship form can be formed between a target diagnosis object and a historical diagnosis object, the case acquisition efficiency is greatly improved, and a data basis is provided for the follow-up promotion of the diagnosis experience of the diagnosis object.
Optionally, in the embodiment of the present application, it may also be determined whether the historical consultation data of the target consultation object is stored in the consultation platform. If the historical inquiry data is stored in the inquiry platform, judging whether the historical inquiry data has first case characteristic information belonging to a first priority in case data or not. If the historical inquiry data has the first case characteristic information, the similarity between the case data and the historical medical record data of the historical consultation object is determined by combining the historical inquiry data, so that the accuracy of the similarity between the case data and the historical medical record data is further improved.
For example, whether the patient has a diagnosis record in the inquiry platform is inquired, and if the patient has the diagnosis record, whether the department who has last been diagnosed is the same as the department who has this diagnosis is judged. If the two consultations are the same, combining the historical inquiry data to determine the similarity between the case data and the historical medical record data of the historical consultation object.
Step S240, generating introduction information of the case to be recommended, and displaying the introduction information to the target doctor-seeing object.
In the embodiment of the present application, the case to be recommended may be understood as a case that is relatively similar to the illness state information of the target patient. Through the case to be recommended, the target patient can be assisted to know own illness state, and more accurate cognition is established on the illness state.
Specifically, an alternative implementation of step S240 is to acquire text information and/or image information associated with the case to be recommended. For example, there may be science popularization articles, science popularization videos, and doctor's consultation procedures related to the case to be recommended. Further alternatively, the presentation form of the introduction information may be selected by the target visit object, such as selecting whether to play a video, or selecting whether to view a science popularization article, or selecting whether to view a doctor's visit procedure in the user interface.
Because privacy information of the patient is often involved in the diagnosis process, in the embodiment of the application, in order to further protect privacy security of the historical patient, sensitive information in the case to be recommended is optionally subjected to desensitization processing, so as to obtain inquiry introduction information for introducing the inquiry process. Desensitization treatments include, but are not limited to: hiding processing, replacing processing and covering processing. Thus, the privacy information of the historical treatment object can be protected through desensitization treatment, illegal users are prevented from performing illegal operations on the privacy information of the historical treatment object, the information security of the treatment object is protected, and the security risk of medical service data is reduced.
According to the information recommendation method of the inquiry platform, firstly, case data of a target consultation object is obtained, and the case data is obtained through pre-inquiry data fed back by the target consultation object. Then, determining the similarity between the case data and the historical medical record data of the historical diagnosis object, and screening the historical medical record data of the historical diagnosis object, wherein the similarity meets the preset condition, so as to obtain the case to be recommended. Through the screening, an efficient data relationship form can be formed between the target treatment object and the historical treatment object, and a data basis is provided for the follow-up promotion of the treatment experience of the treatment object. Finally, the introduction information of the case to be recommended is generated, and the introduction information is displayed to the target doctor-seeing object, so that the automatic case information recommendation of the target doctor-seeing object is realized, the target doctor-seeing object is assisted to establish the primary cognition of the illness state, and the service experience of the doctor-seeing object is effectively improved.
Having described the method of the embodiment of the present application, next, an information recommendation device of a query platform of the embodiment of the present application will be described with reference to fig. 4.
The information recommending apparatus 40 of the inquiry platform according to the embodiment of the present application can implement the steps of the information recommending method corresponding to the inquiry platform according to the embodiment corresponding to fig. 2. The functions performed by the information recommendation means 40 of the inquiry platform may be performed by executing corresponding software. The software includes one or more modules corresponding to the functions described above, which may be software. The information recommending apparatus 40 of the inquiry platform may include an input/output module 401 and a processing module 402, and the function implementation of the processing module 402 and the input/output module 401 may refer to the operations performed in the embodiment corresponding to fig. 2, which are not described herein. For example, the processing module 402 may be configured to control data transceiving operations of the input-output module 401.
In some embodiments, the input-output module 401 is further configured to obtain case data of the target visit object; the case data are obtained through pre-consultation data fed back by the target consultation object;
the processing module 402 is further configured to determine a similarity between the case data and historical medical record data of a historical visit subject; screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended; and generating introduction information of the case to be recommended, and displaying the introduction information to the target doctor-seeing object.
In some embodiments, the processing module 402, when acquiring the case data of the target visit object, is configured to:
receiving pre-consultation data of the target consultation object; the pre-consultation data are input by the target consultation object according to a pre-consultation flow;
extracting the case data from the pre-consultation data;
wherein the case data includes at least one of basic information, affected parts, affected symptoms, time of onset, frequency of onset, status of medical consultation, and patient complaint information of the target consultation subject.
In some embodiments, the processing module 402, when determining the similarity between the case data and the historic medical record data of the historic visit object, is configured to:
Extracting first case feature information belonging to a first priority from the case data according to the preset priority;
taking a history visit object with the first case characteristic information in the history medical record data as a first classification group;
extracting second case feature information belonging to a second priority from the case data; the second priority is lower than the first priority;
extracting third case feature information belonging to the second priority from the historical medical record data of each historical visit object in the first classification group;
calculating the similarity between the second case feature information and the third case feature information;
the preset priority at least comprises: the affected part belongs to one of departments, patient complaints, medicine taking history, operation history, age groups and sexes.
In some embodiments, the processing module 402 is configured to, when obtaining the case to be recommended, screen the historical medical record data of the historical visit object whose similarity meets the preset condition:
selecting a historical visit object, of which the similarity between the second case characteristic information and the third case characteristic information reaches a set threshold, from the first classification group;
And taking the historical medical record data of the historical visit object with the similarity reaching the set threshold value as the case to be recommended.
In some embodiments, the processing module 402 is further configured to:
after extracting the second case feature information belonging to the second priority from the case data, if the history visit object in the first classification group does not have the third case feature information belonging to the second priority
And calculating the similarity between the case data and the historical medical record data of each historical visit object in the first classification group by adopting the case characteristic information of the next priority.
In some embodiments, the processing module 402, when generating the introduction information of the case to be recommended, is configured to:
acquiring text information and/or image information associated with the case to be recommended; and/or the number of the groups of groups,
and desensitizing the sensitive information in the case to be recommended to obtain inquiry introduction information for introducing an inquiry process.
In some embodiments, the processing module 402 is further configured to:
judging whether historical consultation data of the target consultation object is stored in a consultation platform;
if the historical inquiry data are stored in the inquiry platform, judging whether the historical inquiry data have first case characteristic information belonging to a first priority in the case data or not;
And if the historical inquiry data has the first case characteristic information, combining the historical inquiry data to determine the similarity between the case data and the historical medical record data of the historical visit object.
The information recommendation device of the inquiry platform provided by the embodiment of the application can provide the introduction information of similar cases for the doctor based on the pre-inquiry data, and forms a high-efficiency data relationship form between the target doctor object and the history doctor object, thereby realizing the automatic case information recommendation of the target doctor object, assisting the target doctor object to establish the primary cognition of the illness state and effectively improving the service experience of the doctor object.
Having described the method and apparatus of the embodiments of the present application, a description will be given of a computer-readable storage medium of the embodiments of the present application, which may be an optical disc, on which a computer program (i.e., a program product) is stored, which when executed by a processor, implements the steps described in the above-described method embodiments, for example, obtaining case data of a target patient; the case data are obtained through pre-consultation data fed back by the target consultation object; determining the similarity between the case data and the historical medical record data of the historical visit object; screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended; and generating introduction information of the case to be recommended, and displaying the introduction information to the target consultation object. The specific implementation of each step is not repeated here.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The information recommendation device 40 of the inquiry platform in the embodiment of the present application is described above from the point of view of the modularized functional entity, and the server and the terminal device for executing the information recommendation method of the inquiry platform in the embodiment of the present application are described below from the point of view of the device, respectively.
It should be noted that, in the embodiment of the information recommending apparatus of the inquiry platform of the present application, the entity device corresponding to the input/output module 401 shown in fig. 4 may be an input/output unit, a transceiver, a radio frequency circuit, a communication module, an input/output (I/O) interface, etc., and the entity device corresponding to the processing module 402 may be a processor. The information recommending apparatus 40 of the inquiry platform shown in fig. 4 may have a structure as shown in fig. 5, and when the information recommending apparatus 40 of the inquiry platform shown in fig. 4 has a structure as shown in fig. 5, the processor and the transceiver in fig. 5 can implement the same or similar functions as the processing module 402 and the input-output module 401 provided in the foregoing apparatus embodiment corresponding to the apparatus, and the memory in fig. 5 stores a computer program to be called when the processor performs the information recommending method of the inquiry platform described above.
The embodiment of the present application further provides a terminal device, as shown in fig. 6, for convenience of explanation, only the portion relevant to the embodiment of the present application is shown, and specific technical details are not disclosed, please refer to the method portion of the embodiment of the present application. The terminal device may be any terminal device including a mobile phone, a tablet computer, a personal digital assistant (Personal Digital Assistant, PDA), a Point of Sales (POS), a vehicle-mounted computer, and the like, taking the terminal device as an example of the mobile phone:
fig. 6 is a block diagram showing a part of the structure of a mobile phone related to a terminal device provided by an embodiment of the present application. Referring to fig. 6, the mobile phone includes: radio Frequency (RF) circuitry 1010, memory 1020, input unit 1030, display unit 1040, sensor 1050, audio circuitry 1060, wireless fidelity (wireless fidelity, wiFi) module 1070, processor 1080, and power source 1090. Those skilled in the art will appreciate that the handset configuration shown in fig. 6 is not limiting of the handset and may include more or fewer components than shown, or may combine certain components, or may be arranged in a different arrangement of components.
The following describes the components of the mobile phone in detail with reference to fig. 6:
The RF circuit 1010 may be used for receiving and transmitting signals during a message or a call, and particularly, after receiving downlink information of a base station, the signal is processed by the processor 1080; in addition, the data of the design uplink is sent to the base station. Typically, the RF circuitry 1010 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (Low Noise Amplifier, LNA), a duplexer, and the like. In addition, the RF circuitry 1010 may also communicate with networks and other devices via wireless communications. The wireless communications may use any communication standard or protocol including, but not limited to, global system for mobile communications (Global System of Mobile communication, GSM), general packet radio service (General Packet Radio Service, GPRS), code division multiple access (Code Division Multiple Access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA), long term evolution (Long Term Evolution, LTE), email, short message service (Short Messaging Service, SMS), and the like.
Memory 1020 may be used to store software programs and modules that processor 1080 executes by running the software programs and modules stored in memory 1020 to perform various functional applications of the handset and information recommendation of the interrogation platform. The memory 1020 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, memory 1020 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state memory device.
The input unit 1030 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the handset. In particular, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 1031 or thereabout using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel 1031 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 1080 and can receive commands from the processor 1080 and execute them. Further, the touch panel 1031 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 1030 may include other input devices 1032 in addition to the touch panel 1031. In particular, other input devices 1032 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a track ball, a mouse, a joystick, etc.
The display unit 1040 may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit 1040 may include a display panel 1041, and alternatively, the display panel 1041 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 1031 may overlay the display panel 1041, and when the touch panel 1031 detects a touch operation thereon or thereabout, the touch panel is transferred to the processor 1080 to determine a type of touch event, and then the processor 1080 provides a corresponding visual output on the display panel 1041 according to the type of touch event. Although in fig. 6, the touch panel 1031 and the display panel 1041 are two independent components to implement the input and input functions of the mobile phone, in some embodiments, the touch panel 1031 and the display panel 1041 may be integrated to implement the input and output functions of the mobile phone.
The handset may also include at least one sensor 1040, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1041 according to the brightness of ambient light, and the proximity sensor may turn off the display panel 1041 and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the handset are not described in detail herein.
Audio circuitry 1060, a speaker 1061, and a microphone 1062 may provide an audio interface between a user and a cell phone. Audio circuit 1060 may transmit the received electrical signal after audio data conversion to speaker 1061 for conversion by speaker 1061 into an audio signal output; on the other hand, microphone 1062 converts the collected sound signals into electrical signals, which are received by audio circuit 1060 and converted into audio data, which are processed by audio data output processor 1080 for transmission to, for example, another cell phone via RF circuit 1010 or for output to memory 1020 for further processing.
WiFi belongs to a short-distance wireless transmission technology, and a mobile phone can help a user to send and receive emails, browse webpages, access streaming media and the like through a WiFi module 1070, so that wireless broadband Internet access is provided for the user. Although fig. 6 shows a WiFi module 1070, it is understood that it does not belong to the necessary constitution of the handset, and can be omitted entirely as required within the scope of not changing the essence of the invention.
Processor 1080 is the control center of the handset, connects the various parts of the entire handset using various interfaces and lines, and performs various functions and processes of the handset by running or executing software programs and/or modules stored in memory 1020, and invoking data stored in memory 1020, thereby performing overall monitoring of the handset. Optionally, processor 1080 may include one or more processing units; alternatively, processor 1080 may integrate an application processor primarily handling operating systems, user interfaces, applications, etc., with a modem processor primarily handling wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 1080.
The handset further includes a power source 1090 (e.g., a battery) for powering the various components, optionally in logical communication with the processor 1080 via a power management system, such as for managing charge, discharge, and power consumption by the power management system.
Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.
In the embodiment of the present application, the processor 1080 included in the mobile phone further has a control unit for executing the procedure described in the above method embodiment executed by the information recommending apparatus of the inquiry platform.
Fig. 7 is a schematic diagram of a server structure according to an embodiment of the present application, where the server 1100 may have a relatively large difference between configurations or performances, and may include one or more central processing units (central processing units, CPU) 1122 (e.g., one or more processors) and a memory 1132, and one or more storage mediums 1130 (e.g., one or more mass storage devices) storing application programs 1142 or data 1144. Wherein the memory 1132 and the storage medium 1130 may be transitory or persistent. The program stored on the storage medium 1130 may include one or more modules (not shown), each of which may include a series of instruction operations on a server. Still further, the central processor 1122 may be provided in communication with a storage medium 1130, executing a series of instruction operations in the storage medium 1130 on the server 1100.
The Server 1100 may also include one or more power supplies 1120, one or more wired or wireless network interfaces 1150, one or more input-output interfaces 1158, and/or one or more operating systems 1141, such as Windows Server, mac OS X, unix, linux, freeBSD, and the like.
The steps performed by the server in the above embodiments may be based on the structure of the server 1100 shown in fig. 7. For example, the steps performed by the information recommending apparatus 60 of the inquiry platform shown in fig. 7 in the above-described embodiment may be based on the server structure shown in fig. 7. For example, the CPU 1122 may perform the following operations by calling instructions in the memory 1132:
acquiring case data of the target visit object through the input-output interface 1158; the case data are obtained through pre-consultation data fed back by the target consultation object; determining the similarity between the case data and the historical medical record data of the historical visit object; screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended; and generating introduction information of the case to be recommended, and displaying the introduction information to the target consultation object.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, apparatuses and modules described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein.
In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in the form of physical devices or in the form of software functional modules. The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium.
In the above embodiments, it may be implemented in whole or in part by software, physical devices, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the flow or functions according to the embodiments of the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
The above description has been made in detail on the technical solutions provided by the embodiments of the present application, and specific examples are applied in the embodiments of the present application to illustrate the principles and implementation manners of the embodiments of the present application, where the above description of the embodiments is only for helping to understand the methods and core ideas of the embodiments of the present application; meanwhile, as for those skilled in the art, according to the idea of the embodiment of the present application, there are various changes in the specific implementation and application scope, and in summary, the present disclosure should not be construed as limiting the embodiment of the present application.

Claims (10)

1. An information recommendation method of a consultation platform, which is characterized by comprising the following steps:
obtaining case data of a target visit object; the case data are obtained through pre-consultation data fed back by the target consultation object;
determining the similarity between the case data and the historical medical record data of the historical visit object;
screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended;
and generating introduction information of the case to be recommended, and displaying the introduction information to the target doctor-seeing object.
2. The method of claim 1, wherein acquiring case data for the target visit object comprises:
Receiving pre-consultation data of the target consultation object; the pre-consultation data are input by the target consultation object according to a pre-consultation flow;
extracting the case data from the pre-consultation data;
wherein the case data includes at least one of basic information, affected parts, affected symptoms, time of onset, frequency of onset, status of medical consultation, and patient complaint information of the target consultation subject.
3. The method of claim 1, wherein determining the similarity between the case data and the historical medical record data of the historical visit subject comprises:
extracting first case feature information belonging to a first priority from the case data according to the preset priority;
taking a history visit object with the first case characteristic information in the history medical record data as a first classification group;
extracting second case feature information belonging to a second priority from the case data; the second priority is lower than the first priority;
extracting third case feature information belonging to the second priority from the historical medical record data of each historical visit object in the first classification group;
Calculating the similarity between the second case feature information and the third case feature information;
the preset priority at least comprises: the affected part belongs to one of departments, patient complaints, medicine taking history, operation history, age groups and sexes.
4. The method of claim 3, wherein screening the historical medical record data of the historical visit object with similarity meeting the preset condition to obtain the case to be recommended comprises:
selecting a historical visit object, of which the similarity between the second case characteristic information and the third case characteristic information reaches a set threshold, from the first classification group;
and taking the historical medical record data of the historical visit object with the similarity reaching the set threshold value as the case to be recommended.
5. The method of any one of claims 3 or 4, further comprising, after extracting second case feature information belonging to a second priority from the case data:
if the historical visit object in the first classification group does not have the third case feature information belonging to the second priority
And calculating the similarity between the case data and the historical medical record data of each historical visit object in the first classification group by adopting the case characteristic information of the next priority.
6. The method of claim 1, wherein generating introduction information for the case to be recommended comprises:
acquiring text information and/or image information associated with the case to be recommended; and/or the number of the groups of groups,
and desensitizing the sensitive information in the case to be recommended to obtain inquiry introduction information for introducing an inquiry process.
7. The method as recited in claim 1, further comprising:
judging whether historical consultation data of the target consultation object is stored in a consultation platform;
if the historical inquiry data are stored in the inquiry platform, judging whether the historical inquiry data have first case characteristic information belonging to a first priority in the case data or not;
and if the historical inquiry data has the first case characteristic information, combining the historical inquiry data to determine the similarity between the case data and the historical medical record data of the historical visit object.
8. An information recommendation device of a consultation platform, the device comprising:
the input-output module is configured to acquire case data of a target treatment object; the case data are obtained through pre-consultation data fed back by the target consultation object;
A processing module configured to determine a similarity between the case data and historical medical record data of the historical visit object; screening the historical medical record data of the historical diagnosis object with similarity meeting preset conditions to obtain a case to be recommended; and generating introduction information of the case to be recommended, and displaying the introduction information to the target doctor-seeing object.
9. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-7 when the computer program is executed.
10. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any of claims 1-7.
CN202310436916.0A 2023-04-21 2023-04-21 Information recommendation method, related device and storage medium of inquiry platform Pending CN117093766A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117709493A (en) * 2023-12-26 2024-03-15 江苏道朴网络科技有限公司 Intelligent medical inquiry system based on historical pathology
CN118053598A (en) * 2024-04-16 2024-05-17 万链指数(青岛)信息科技有限公司 Medical information sharing method and system based on medical big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117709493A (en) * 2023-12-26 2024-03-15 江苏道朴网络科技有限公司 Intelligent medical inquiry system based on historical pathology
CN118053598A (en) * 2024-04-16 2024-05-17 万链指数(青岛)信息科技有限公司 Medical information sharing method and system based on medical big data

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