CN113436725B - Data processing method, system, computer device and computer readable storage medium - Google Patents
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Abstract
The embodiment of the invention provides a data processing method, which comprises the following steps: acquiring a plurality of user basic data, medical data and user behavior data of a target user; extracting a plurality of disease risk factors corresponding to the target disease from a plurality of user basic data, medical data and user behavior data; invoking a preset prior probability and a preset diagnosis rule associated with a target disease, and calculating to obtain a plurality of conditional probabilities corresponding to the target disease; inputting a plurality of disease risk factors and a plurality of conditional probabilities into a disease risk assessment model to output a disease risk value of a target user for a target disease; medical diagnostic data is generated based on the risk of illness value. According to the embodiment of the invention, the illness probability of the target user about the target disease is obtained through analysis from a plurality of dimensions related to the target user, such as daily health, medical data and user behaviors of the user, the multi-dimensional user data is effectively utilized, and the accuracy of the medical diagnosis result of the target user is improved.
Description
Technical Field
Embodiments of the present invention relate to the field of big data technologies, and in particular, to a data processing method, a system, a computer device, and a computer readable storage medium.
Background
With the increasing medical level, the health requirements of people are also increasing. Currently, diagnosis of diseases is often performed by analyzing medical examination data and medical record data of users in a medical system to obtain medical diagnosis data. However, the diagnostic methods for the above-mentioned diseases have at least the following drawbacks: the daily health data and the user behavior data of the user are not considered, so that the medical diagnosis conclusion has low accuracy due to the fact that the disease diagnosis lacks an effective data basis because the user information with multiple dimensions cannot be obtained easily.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a data processing method, system, computer device and computer readable storage medium, which are used for solving the problem of low accuracy of medical diagnosis conclusion due to lack of multi-dimensional user information in the aspect of data processing applied to disease diagnosis.
The embodiment of the invention solves the technical problems through the following technical scheme:
a data processing method, comprising:
Acquiring a plurality of user data of a target user, wherein the plurality of user data comprises a plurality of user basic data, a plurality of medical data and a plurality of user behavior data;
extracting a plurality of disease risk factors corresponding to a target disease from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data;
invoking a preset prior probability associated with the target disease and a preset diagnosis rule associated with the target disease, and calculating a plurality of conditional probabilities corresponding to the target disease;
inputting the plurality of disease risk factors and the plurality of conditional probabilities into a disease risk assessment model, and outputting a disease risk value of the target user for the target disease through the disease risk assessment model; and
And generating medical diagnosis data according to the disease risk value, wherein the medical diagnosis data is used for representing the existence or non-existence of the disease risk of the target disease of the target user.
Optionally, the acquiring the plurality of user data of the target user includes:
traversing a database according to a query request to determine a target position of the target user in the database;
Extracting a plurality of original user data of the target user from the database according to a preset time period and a target position of the target user in the database; and
And converting the plurality of original user data according to a preset format rule to obtain a plurality of user data.
Optionally, the extracting a plurality of disease risk factors corresponding to the target disease from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data includes:
and performing correlation calculation on the plurality of user basic data, the plurality of medical data and the plurality of user behavior data to extract a plurality of disease risk factors corresponding to the target diseases from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data.
Optionally, the performing correlation calculation on the plurality of user basic data, the plurality of medical data and the plurality of user behavior data to extract a plurality of disease risk factors corresponding to the target disease from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data includes:
Extracting any two user basic data from the plurality of user basic data to form a plurality of first data sets, and calculating to obtain a first correlation coefficient corresponding to each first data set according to the two corresponding user basic data in each first data set, wherein the first correlation coefficient is used for representing the similarity between the two user basic data corresponding to the first correlation coefficient;
extracting any two medical data from the plurality of medical data, combining the medical data to form a plurality of second data groups, and calculating a second correlation number corresponding to each group of second data groups according to the two medical data corresponding to each group of second data groups, wherein the second correlation number is used for representing similarity between the two medical data corresponding to the second correlation number;
extracting any two user behavior data from the plurality of user behavior data, combining the user behavior data to form a plurality of third data sets, and calculating a third phase relation number corresponding to each third data set according to the two user behavior data corresponding to each third data set, wherein the third phase relation number is used for representing the similarity between the two user behavior data corresponding to the third phase relation number;
Judging whether the first correlation coefficients, the second correlation coefficients and the third correlation coefficients meet preset conditions or not;
determining a first data set with a first correlation coefficient meeting the preset condition as a first target data set, wherein two corresponding user basic data in each first target data set are in a strong correlation relationship; determining a second data set with a second correlation number meeting the preset condition as a second target data set, wherein two corresponding medical data in each second target data set are in a strong correlation; determining a third data set with a third relation number meeting the preset condition as a third target data set, wherein two corresponding user behavior data in each third target data set are in a strong relation; and
And deleting any one user basic data in each group of first target data groups, deleting any one medical data in each group of second target data groups and deleting any one user behavior data in each group of third target data groups respectively to obtain a plurality of illness risk factors, wherein the plurality of illness risk factors comprise one user basic data remained in each group of first target data groups, one medical data remained in each group of second target data groups and one user behavior data remained in each group of third target data groups.
Optionally, the first correlation coefficient, the second correlation coefficient and the third correlation coefficient are all calculated by pearson integration algorithm.
Optionally, the step of generating medical diagnosis data according to the disease risk value further includes:
comparing the disease risk value with a preset risk threshold value; and
And generating medical diagnosis data according to the comparison result.
Optionally, the disease evaluation risk model is constructed according to a bayesian algorithm.
To achieve the above object, an embodiment of the present invention further provides a data processing system, including:
an acquisition module for acquiring a plurality of user data of a target user, wherein the plurality of user data includes a plurality of user basic data, a plurality of medical data, and a plurality of user behavior data;
the extraction module is used for extracting a plurality of disease risk factors corresponding to the target diseases from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data;
the first calculation module is used for calling a preset priori probability associated with the target disease and a preset diagnosis rule associated with the target disease, and calculating a plurality of conditional probabilities corresponding to the target disease; and
The second calculation module is used for inputting the multiple disease risk factors and the multiple conditional probabilities into a disease risk assessment model, and outputting a disease risk value of the target user for the target disease through the disease risk assessment model; and
And the generation module is used for generating medical diagnosis data according to the disease risk value, wherein the medical diagnosis data is used for indicating the existence or non-existence of the disease risk of the target disease of the target user.
To achieve the above object, an embodiment of the present invention also provides a computer apparatus including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the data processing method as described above when executing the computer program.
To achieve the above object, an embodiment of the present invention also provides a computer-readable storage medium having stored therein a computer program executable by at least one processor to cause the at least one processor to perform the steps of the data processing method as described above.
According to the data processing method, the system, the computer equipment and the computer readable storage medium provided by the embodiment of the invention, a plurality of illness risk factors extracted from the user basic data, the medical data and the user behavior data are calculated according to the preset priori probabilities and the preset diagnosis rules to obtain a plurality of conditional probabilities corresponding to a target disease, the conditional probabilities are input into an illness risk assessment model, and an illness risk value of the target user for the target disease is output through the illness risk assessment model; the probability of the target user about the target disease is obtained through analysis of multiple dimensions (such as daily health, medical data and user behaviors) related to the target user, the multi-dimensional user data is effectively utilized, and the accuracy of the medical diagnosis result of the target user is improved.
The invention will now be described in more detail with reference to the drawings and specific examples, which are not intended to limit the invention thereto.
Drawings
FIG. 1 is a flowchart illustrating a data processing method according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps for acquiring a plurality of user data of a target user in a data processing method according to a first embodiment of the present invention;
FIG. 3 is a flowchart showing steps for extracting a plurality of risk factors from the user basic data, the medical data and the user behavior data in a data processing method according to a first embodiment of the present invention;
FIG. 4 is a flowchart showing steps for performing correlation calculation on a plurality of user data to obtain a plurality of risk factors in a data processing method according to a first embodiment of the present invention;
FIG. 5 is a block diagram illustrating a data processing system according to a second embodiment of the present invention;
fig. 6 is a schematic hardware structure of a computer device according to a third embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It should be noted that the descriptions of "first," "second," etc. in the embodiments of the present application are for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
Example 1
Referring to FIG. 1, a flowchart illustrating steps of a data processing method according to an embodiment of the invention is shown. It will be appreciated that the flow charts in the method embodiments are not intended to limit the order in which the steps are performed. The following description is exemplary with a computer device as an execution subject, and specifically follows:
the data processing method is applied to a medical data processing platform.
As shown in fig. 1, the data processing method may include steps S100 to S108, in which:
Step S100, a plurality of user data of a target user are acquired, wherein the plurality of user data comprise a plurality of user basic data, a plurality of medical data and a plurality of user behavior data.
Wherein the plurality of user base data includes, but is not limited to, gender, age, height, weight, etc. data; the plurality of medical data includes, but is not limited to, data of past tumor treatment conditions, past other medical history, allergy history, family medical history, symptoms, vital signs, physical examination, imaging examination, pathology examination, laboratory examination, and the like; the plurality of user behavior data includes, but is not limited to, life habit, work place, frequently accessed place, job site, etc.
In an exemplary embodiment, the method further comprises: a plurality of user data of a plurality of sample users is collected in advance. Illustratively, the pre-collection of the plurality of user data for the plurality of sample users may be accomplished in two ways:
(1) And the external interface of the third party platform is linked through the data interface of the medical data processing platform so as to acquire a plurality of user data of a plurality of sample users from the third party platform through the connection of the interfaces at two ends. The third party platform may be, among other things, various databases of the world health organization epidemiological data system (whois). The third party platform may also be a blockchain platform in which user data for a plurality of sample users is stored in individual blocks of the blockchain platform.
(2) And directly extracting a plurality of user data of a plurality of sample users from an underlying database which is independently stored in the medical data processing platform.
The computer equipment periodically or in real time crawls a plurality of user data corresponding to each user from a bottom database of the medical data processing platform or a third party platform; classifying the multiple user data corresponding to each user according to the attribute of each user data, wherein the attribute corresponding to gender, age, height, weight and the like is basic attribute and classified as basic data; the corresponding attributes of the previous tumor treatment conditions, the previous other medical histories, allergy histories, family medical histories, symptoms, vital signs, physical examination, imaging examination, pathological examination, laboratory examination and the like are medical attributes and classified as medical data; the corresponding attributes of life habits, workplaces, places which are frequently accessed, job sites and the like are behavior attributes and classified as behavior data. And caching each user data of the classified users into a database of a cache server according to key-value (key-value pair).
In an exemplary embodiment, the method further comprises preprocessing the crawled plurality of user data for each user, including: and discretizing attribute values of each of the plurality of sample user basic data, the plurality of sample medical data and the plurality of sample user behavior data of each user.
In order to improve the data processing efficiency, referring to fig. 2, the operation of acquiring the plurality of user data of the target user may include steps S200 to S204, wherein: step S200, traversing a database according to a query request to determine a target position of the target user in the database; step S202, extracting a plurality of original user data of the target user from the database according to a preset time period and the target position of the target user in the database; and step S204, converting the plurality of original user data according to a preset format rule to obtain a plurality of user data. Determining target positions of original user data (i.e. value) of a target user in a database (i.e. a database in a cache server) according to user names (i.e. keys) of the target user, and extracting the original user data, such as user basic data, medical data and user behavior data, according to the target positions; and converting each original user data into corresponding user data in a preset format, namely a Json format character string, through a JSON.stringing (obj) function. Illustratively, user basic data, medical data, and user behavior data of a user are categorized into different data sets in a database by different data types in the database.
For example, the preset time period may be a preset time period, such as five years. It should be noted that different preset time periods may be set according to different cancer types. In other exemplary embodiments, the preset time period may be set as an adjustable parameter; the dynamic adjustment of the preset time period enables the acquired user data in the preset time period to reflect the change of the user in the preset time period, meanwhile, the calculated amount of the computer equipment is reduced, and the calculation resources are saved.
Step S102, extracting a plurality of disease risk factors corresponding to the target diseases from the user basic data, the medical data and the user behavior data.
In order to ensure the accuracy, reliability and stability of the obtained disease conclusion of the target disease by utilizing the relation between the data, the disease risk factors (i.e. disease risk factors) of the target disease need to be extracted in advance. The disease risk factor may be understood as a risk factor that causes a user to suffer from a target disease. In an exemplary embodiment, the extracting a plurality of risk factors from the user base data, the medical data, and the user behavior data includes: and performing correlation calculation on the user basic data, the medical data and the user behavior data to extract a plurality of disease risk factors corresponding to the target diseases from the user basic data, the medical data and the user behavior data. For example, referring to fig. 3, extracting a plurality of risk factors from the plurality of user base data, the plurality of medical data, and the plurality of user behavior data may also be obtained by: step S300, extracting any two user basic data from the plurality of user basic data to form a plurality of first data groups, and calculating to obtain a first correlation coefficient corresponding to each group of first data groups according to the two corresponding user basic data in each group of first data groups, wherein the first correlation coefficient is used for representing the similarity between the two user basic data corresponding to the first correlation coefficient; step S302, extracting any two medical data from the plurality of medical data to form a plurality of second data groups, and calculating a second correlation number corresponding to each group of second data groups according to the two medical data corresponding to each group of second data groups, wherein the second correlation number is used for representing the similarity between the two medical data corresponding to the second correlation number; step S304, any two user behavior data are extracted from the plurality of user behavior data to be combined to form a plurality of third data groups, and a third phase relation number corresponding to each third data group is calculated according to the two user behavior data corresponding to each third data group, wherein the third phase relation number is used for representing the similarity between the two user behavior data corresponding to the third phase relation number; step S306, judging whether the first correlation coefficients, the second correlation coefficients and the third correlation coefficients meet preset conditions; step S308, determining a first data set with a first correlation coefficient meeting the preset condition as a first target data set, wherein two corresponding user basic data in each first target data set are in a strong correlation relationship; determining a second data set with a second correlation number meeting the preset condition as a second target data set, wherein two corresponding medical data in each second target data set are in a strong correlation; determining a third data set with a third relation number meeting the preset condition as a third target data set, wherein two corresponding user behavior data in each third target data set are in a strong relation; and step S310, deleting any one user basic data in each group of first target data groups, deleting any one medical data in each group of second target data groups and deleting any one user behavior data in each group of third target data groups respectively to obtain a plurality of illness risk factors, wherein the plurality of illness risk factors comprise one user basic data remained in each group of first target data groups, one medical data remained in each group of second target data groups and one user behavior data remained in each group of third target data groups. Illustratively, the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are all calculated by Pearson's product (Pearson) algorithm. Respectively comparing each first correlation coefficient, each second correlation coefficient and each third correlation coefficient with a preset correlation threshold value; according to the comparison result of each first correlation coefficient, each second correlation coefficient and each third correlation coefficient with a preset correlation threshold value, extracting a plurality of disease risk factors from the user basic data, the medical data and the user behavior data respectively: if the first correlation coefficient is larger than a preset correlation threshold, two user basic data corresponding to the first correlation coefficient are shown to be in a strong correlation relationship, and one user basic data is selected as a disease risk factor; if the second correlation number is larger than the preset correlation threshold, two medical data corresponding to the second correlation number are shown to be in a strong correlation, and one medical data is selected as a disease risk factor; if the third relation number is larger than the preset correlation threshold, the two user behavior data corresponding to the third relation number are shown to be in a strong correlation relation, and one user behavior data is selected as a disease risk factor. The preset correlation threshold may be set to 0.8 when the correlation analysis is performed using pearson correlation coefficients. In other exemplary embodiments, the plurality of correlation coefficients may also be calculated by cosine similarity.
Step S104, a preset prior probability associated with the target disease and a preset diagnosis rule associated with the target disease are called, and a plurality of conditional probabilities corresponding to the target disease are calculated.
The prior probability is a probability that the target user suffers from the target disease, which is preset before each user data of the target user is processed, and is used as a reference parameter for finally judging whether the user suffers from the target disease. The preset diagnosis rules associated with the target disease are analyzed according to sample diagnosis conclusion data of a plurality of sample users associated with the target disease and sample actual disease data, wherein the sample diagnosis conclusion data comprises negative and positive; sample actual disease data includes disease and health. For example, analyzing sample diagnostic conclusion data and sample actual disease data for a plurality of sample users associated with a target disease a may divide the plurality of sample users into four categories: (1) Sample diagnosis conclusion data are expressed as positive, and sample actual disease data are expressed as disease; (2) Sample diagnosis conclusion data are expressed as positive, and sample actual disease data are expressed as healthy; (3) Sample diagnosis conclusion data are expressed as negative, and sample actual disease data are expressed as probability of disease; (4) Sample diagnostic conclusion data is indicated as negative and sample actual disease data is indicated as healthy. The preset diagnosis rules are used for calculating the duty ratio probability of a plurality of sample users under the four conditions. For example: sample diagnosis conclusion data are expressed as positive, and the probability that the actual disease data of the sample are expressed as disease is 0.95; the sample diagnosis conclusion data is represented as positive, the probability that the sample actual disease data is represented as healthy is 0.05, the sample diagnosis conclusion data is represented as negative, the probability that the sample actual disease data is represented as diseased is 0.02, the sample diagnosis conclusion data is represented as negative, and the probability that the sample actual disease data is represented as healthy is 0.98.
In an exemplary embodiment, each conditional probability may be found by the following formula:
wherein P represents a conditional probability; x represents negative or positive; y represents a disease or health condition,
representative are several cases of cancer and positive, cancer and negative, positive in healthy state or negative in healthy state; d represents the number of sample users; α=1 means calculation from the first sample user. For example, four mutually independent conditional probabilities corresponding to the target disease a are calculated, wherein: (1) sample diagnosis conclusion data is represented as positive and sample actual disease data is represented as having a conditional probability of 0.095% for disease correspondence, (2) sample diagnosis conclusion data is represented as negative and sample actual disease data is represented as having a conditional probability of 0.005% for disease correspondence, (3) sample diagnosis conclusion data is represented as positive and sample actual disease data is represented as having a conditional probability of 1.998% for health correspondence and (4) sample diagnosis conclusion data is represented as negative and sample actual disease data is represented as having a conditional probability of 97.902% for health correspondence.
Step S106, inputting the plurality of disease risk factors and the plurality of conditional probabilities into a disease risk assessment model, and outputting a disease risk value of the target user for the target disease through the disease risk assessment model.
The disease evaluation risk model is constructed according to a Bayesian algorithm.
The disease risk value is calculated based on a bayesian algorithm according to a ratio of a conditional probability that sample diagnosis conclusion data is positive and sample actual disease data is positive to a conditional probability that sample diagnosis conclusion data is healthy and a weight parameter that a plurality of disease risk factors correspond to.
And step S108, generating medical diagnosis data according to the disease risk value, wherein the medical diagnosis data is used for representing the existence or non-existence of the disease risk of the target disease of the target user.
In an exemplary embodiment, referring to fig. 4, step S108 may further include steps S400 to S402, wherein: step S400, comparing the disease risk value with a preset risk threshold; and step S402, generating medical diagnosis data according to the comparison result. If the disease risk value is larger than a preset risk threshold value, the medical diagnosis data indicate that the target user has the disease risk of the target disease; and if the disease risk value is smaller than the preset risk threshold value, the medical diagnosis data indicate that the target user does not have the disease risk of the target disease.
In an exemplary embodiment, the method further comprises: uploading the disease risk value of the target user for the target disease into a blockchain.
The summary information is specifically obtained by performing hash processing on the target user and the disease risk value of the target user for the target disease, for example, by using a sha256s algorithm. Uploading summary information to the blockchain can ensure its security and fair transparency to the user. The user device may download the summary information from the blockchain to verify whether its risk value for the target disease has been tampered with. The blockchain referred to in this example is a novel mode of application for computer technology such as distributed data storage, point-to-point transmission, consensus mechanisms, encryption algorithms, and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
According to the data processing method provided by the embodiment of the invention, a plurality of illness risk factors extracted from the user basic data, the medical data and the user behavior data are calculated according to the preset prior probability and the preset diagnosis rule to obtain a plurality of conditional probabilities corresponding to a target disease, the conditional probabilities are input into an illness risk assessment model, and an illness risk value of the target user for the target disease is output through the illness risk assessment model; the disease probability of the target user about the target disease is obtained through analysis from multiple dimensions related to the target user, such as daily health, medical data and user behaviors of the user, the multi-dimensional user data is effectively utilized, and the accuracy of the medical diagnosis result of the target user is improved.
Example two
With continued reference to FIG. 5, a schematic diagram of program modules of the data processing system of the present invention is shown. In this embodiment, the data processing system 20 may include or be partitioned into one or more program modules that are stored in a storage medium and executed by one or more processors to perform the present invention and implement the data processing methods described above. Program modules depicted in the embodiments of the present invention are directed to a series of computer program instruction segments capable of performing the specified functions and are more suitable than the programs themselves for describing the execution of data processing system 20 in a storage medium. The following description will specifically describe functions of each program module of the present embodiment:
An obtaining module 500, configured to obtain a plurality of user data of a target user, where the plurality of user data includes a plurality of user basic data, a plurality of medical data, and a plurality of user behavior data;
an extracting module 502, configured to extract a plurality of disease risk factors corresponding to the target disease from the plurality of user basic data, the plurality of medical data, and the plurality of user behavior data;
a first calculation module 504, configured to invoke a preset prior probability associated with the target disease and a preset diagnostic rule associated with the target disease, and calculate a plurality of conditional probabilities corresponding to the target disease;
a second calculation module 506, configured to input the multiple disease risk factors and the multiple conditional probabilities into a disease risk assessment model, and output a disease risk value of the target user for the target disease through the disease risk assessment model; and
A generating module 508 is configured to generate medical diagnosis data according to the disease risk value, where the medical diagnosis data is used to indicate the presence or absence of the disease risk of the target user.
In an exemplary embodiment, the obtaining module 500 is further configured to: traversing a database according to a query request to determine target positions of all user data of the target user in the database; extracting the plurality of user data of the target user from the database according to a preset time period and a target position of the target user in the database; and converting the plurality of user data according to a preset format rule to obtain a plurality of user data in a preset format.
In an exemplary embodiment, the extraction module 502 is further configured to: and performing correlation calculation on the plurality of user basic data, the plurality of medical data and the plurality of user behavior data to extract a plurality of disease risk factors corresponding to the target diseases from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data.
In an exemplary embodiment, the extraction module 502 is further configured to: extracting any two user basic data from the plurality of user basic data to form a plurality of first data sets, and calculating to obtain a first correlation coefficient corresponding to each first data set according to the two corresponding user basic data in each first data set, wherein the first correlation coefficient is used for representing the similarity between the two user basic data corresponding to the first correlation coefficient; extracting any two medical data from the plurality of medical data, combining the medical data to form a plurality of second data groups, and calculating a second correlation number corresponding to each group of second data groups according to the two medical data corresponding to each group of second data groups, wherein the second correlation number is used for representing similarity between the two medical data corresponding to the second correlation number; extracting any two user behavior data from the plurality of user behavior data, combining the user behavior data to form a plurality of third data sets, and calculating a third phase relation number corresponding to each third data set according to the two user behavior data corresponding to each third data set, wherein the third phase relation number is used for representing the similarity between the two user behavior data corresponding to the third phase relation number; judging whether the first correlation coefficients, the second correlation coefficients and the third correlation coefficients meet preset conditions or not; determining a first data set with a first correlation coefficient meeting the preset condition as a first target data set, wherein two corresponding user basic data in each first target data set are in a strong correlation relationship; determining a second data set with a second correlation number meeting the preset condition as a second target data set, wherein two corresponding medical data in each second target data set are in a strong correlation; determining a third data set with a third relation number meeting the preset condition as a third target data set, wherein two corresponding user behavior data in each third target data set are in a strong relation; and deleting any one of the user basic data in each group of the first target data group, deleting any one of the medical data in each group of the second target data group and deleting any one of the user behavior data in each group of the third target data group respectively to obtain a plurality of disease risk factors, wherein the plurality of disease risk factors comprise one of the user basic data remaining in each group of the first target data group, one of the medical data remaining in each group of the second target data group and one of the user behavior data remaining in each group of the third target data group.
In an exemplary embodiment, the generating module 508 is further configured to: comparing the disease risk value with a preset risk threshold value; and generating medical diagnosis data according to the comparison result.
In an exemplary embodiment, the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are all calculated by pearson's integration algorithm.
In an exemplary embodiment, the disease assessment risk model is constructed according to a bayesian algorithm.
In the exemplary embodiment, data processing system 20 also includes an upload module (not identified) that is configured to: uploading the disease risk value of the target user for the target disease into a blockchain.
Example III
Referring to fig. 6, a hardware architecture diagram of a computer device according to a third embodiment of the present invention is shown. In this embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server, or a rack server (including a stand-alone server, or a server cluster made up of multiple servers), or the like. As shown in fig. 6, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and a data processing system 20, which are communicatively coupled to each other via a system bus. Wherein:
In this embodiment, the memory 21 includes at least one type of computer-readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 2. Of course, the memory 21 may also include both internal storage units of the computer device 2 and external storage devices. In this embodiment, the memory 21 is typically used to store program codes and the like of the data processing system 20 of the above embodiment, as well as various types of application software and an operating system installed on the computer device 2. Further, the memory 21 may be used to temporarily store various types of data that have been output or are to be output.
The processor 22 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 2. In this embodiment, the processor 22 is configured to execute the program code stored in the memory 21 or process data, for example, execute the data processing system 20, to implement the data processing method of the above embodiment.
The network interface 23 may comprise a wireless network interface or a wired network interface, which network interface 23 is typically used for establishing a communication connection between the computer apparatus 2 and other electronic devices. For example, the network interface 23 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be an Intranet (Intranet), the Internet (Internet), a global system for mobile communications (Global System of Mobile communication, GSM), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA), a 4G network, a 5G network, bluetooth (Bluetooth), wi-Fi, or other wireless or wired network.
It is noted that fig. 6 only shows a computer device 2 having components 20-23, but it is understood that not all of the illustrated components are required to be implemented, and that more or fewer components may alternatively be implemented.
In this embodiment, the data processing system 20 stored in the memory 21 may also be divided into one or more program modules, which are stored in the memory 21 and executed by one or more processors (the processor 22 in this embodiment) to complete the present invention.
For example, FIG. 5 shows a schematic diagram of program modules for implementing a second embodiment of the data processing system 20, where the data processing system 20 may be divided into an acquisition module 500, an extraction module 502, a first calculation module 504, a second calculation module 506, and a generation module 508. Program modules depicted herein, being indicative of a sequence of computer program instruction segments, which can perform particular functions, are better suited to describing the execution of the data processing system 20 by the computer device 2. The specific functions of the program modules 500-508 are described in detail in the second embodiment, and are not described herein.
Example IV
The present embodiment also provides a computer-readable storage medium such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor, performs the corresponding functions. The computer readable storage medium of the present embodiment is used to store the data processing system 20, and when executed by a processor, implements the data processing method of the above embodiment.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A method of data processing, comprising:
acquiring a plurality of user data of a target user, wherein the plurality of user data comprises a plurality of user basic data, a plurality of medical data and a plurality of user behavior data;
extracting a plurality of disease risk factors corresponding to a target disease from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data;
invoking a preset prior probability associated with the target disease and a preset diagnosis rule associated with the target disease, and calculating to obtain a plurality of conditional probabilities corresponding to the target disease, wherein the preset diagnosis rule associated with the target disease is obtained by analyzing sample diagnosis conclusion data and sample actual disease data of a plurality of sample users associated with the target disease;
inputting the multiple illness risk factors and the multiple conditional probabilities into an illness risk assessment model, outputting an illness risk value of the target user for the target disease through the illness risk assessment model, wherein the illness risk value is calculated based on a Bayesian algorithm according to the condition probability that the sample diagnosis conclusion data is positive, the sample actual illness data is positive and the sample diagnosis conclusion data is healthy, and the ratio of the condition probability that the sample actual illness data is healthy and the weight parameters corresponding to the multiple illness risk factors; and
And generating medical diagnosis data according to the disease risk value, wherein the medical diagnosis data is used for representing the existence or non-existence of the disease risk of the target disease of the target user.
2. The data processing method according to claim 1, wherein the acquiring the plurality of user data of the target user includes:
traversing a database according to a query request to determine a target position of the target user in the database;
extracting a plurality of original user data of the target user from the database according to a preset time period and a target position of the target user in the database; and
And converting the plurality of original user data according to a preset format rule to obtain a plurality of user data.
3. The data processing method according to claim 1, wherein the extracting a plurality of disease risk factors corresponding to a target disease from the plurality of user basic data, the plurality of medical data, and the plurality of user behavior data includes:
and performing correlation calculation on the plurality of user basic data, the plurality of medical data and the plurality of user behavior data to extract a plurality of disease risk factors corresponding to the target diseases from the plurality of user basic data, the plurality of medical data and the plurality of user behavior data.
4. The data processing method according to claim 3, wherein the performing correlation calculation on the plurality of user basic data, the plurality of medical data, and the plurality of user behavior data to extract a plurality of disease risk factors corresponding to a target disease from the plurality of user basic data, the plurality of medical data, and the plurality of user behavior data includes:
extracting any two user basic data from the plurality of user basic data to form a plurality of first data sets, and calculating to obtain a first correlation coefficient corresponding to each first data set according to the two corresponding user basic data in each first data set, wherein the first correlation coefficient is used for representing the similarity between the two user basic data corresponding to the first correlation coefficient;
extracting any two medical data from the plurality of medical data, combining the medical data to form a plurality of second data groups, and calculating a second correlation number corresponding to each group of second data groups according to the two medical data corresponding to each group of second data groups, wherein the second correlation number is used for representing similarity between the two medical data corresponding to the second correlation number;
Extracting any two user behavior data from the plurality of user behavior data, combining the user behavior data to form a plurality of third data sets, and calculating a third phase relation number corresponding to each third data set according to the two user behavior data corresponding to each third data set, wherein the third phase relation number is used for representing the similarity between the two user behavior data corresponding to the third phase relation number;
judging whether the first correlation coefficients, the second correlation coefficients and the third correlation coefficients meet preset conditions or not;
determining a first data set with a first correlation coefficient meeting the preset condition as a first target data set, wherein two corresponding user basic data in each first target data set are in a strong correlation relationship; determining a second data set with a second correlation number meeting the preset condition as a second target data set, wherein two corresponding medical data in each second target data set are in a strong correlation; determining a third data set with a third relation number meeting the preset condition as a third target data set, wherein two corresponding user behavior data in each third target data set are in a strong relation; and
And deleting any one user basic data in each group of first target data groups, deleting any one medical data in each group of second target data groups and deleting any one user behavior data in each group of third target data groups respectively to obtain a plurality of illness risk factors, wherein the plurality of illness risk factors comprise one user basic data remained in each group of first target data groups, one medical data remained in each group of second target data groups and one user behavior data remained in each group of third target data groups.
5. The data processing method according to claim 4, wherein the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are all calculated by pearson's integration algorithm.
6. The data processing method according to claim 1, wherein the step of generating medical diagnosis data from the disease risk value further comprises:
comparing the disease risk value with a preset risk threshold value; and
And generating medical diagnosis data according to the comparison result.
7. The data processing method according to claim 1, wherein the disease assessment risk model is constructed according to a bayesian algorithm.
8. A data processing system, comprising:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring a plurality of user data of a target user, wherein the user data comprises user basic data, medical data and user behavior data;
an extraction module for extracting a plurality of risk factors from the user base data, the medical data, and the user behavior data;
the first calculation module is used for retrieving a preset prior probability associated with a target disease and a preset diagnosis rule associated with the target disease, calculating to obtain a plurality of conditional probabilities corresponding to the target disease, wherein the preset diagnosis rule associated with the target disease is obtained by analyzing sample diagnosis conclusion data and sample actual diseased data of a plurality of sample users associated with the target disease; and
And the second calculation module is used for inputting the multiple illness risk factors and the multiple conditional probabilities into an illness risk assessment model, outputting an illness risk value of the target user for the target disease through the illness risk assessment model, wherein the illness risk value is based on a Bayesian algorithm, and is calculated according to the condition probability that sample actual illness data is expressed as illness corresponding to the sample diagnosis conclusion data is positive, the ratio of the condition probability that sample actual illness data is expressed as health corresponding to the condition probability, the multiple illness risk factors and weight parameters corresponding to the multiple illness risk factors.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the data processing method according to any of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program executable by at least one processor to cause the at least one processor to perform the steps of the data processing method according to any one of claims 1 to 7.
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