Disease risk information monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a disease risk information monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of medical health and big data, and the method comprises the steps: obtaining physical examination indexes of a user, the physical examination indexes including abnormal physical examination indexes; based on the physical examination indexes, target questions corresponding to the abnormal physical examination indexes are selected from a preset question set, and any question in the preset question set corresponds to a disease factor; determining a target disease factor of the user according to a received feedback result of the user on the target question; and performing disease risk information monitoring on the user based on the target disease factor. The subjective data of the user can be obtained based on the user feedback result, the physical examination result of the user is supplemented, personalized disease risk information monitoring is carried out on the user, reference is provided for the health of the user, and the attention of the user to the health of the user is improved.

Description

Technical Field

[0001] The present application relates to the fields of medical health and big data technology, and in particular to a disease risk information monitoring method, device, electronic device and storage medium. Background Art

[0002] Health checkups, as the primary means for people to understand their physical condition and disease risks, play a crucial role in daily life. With the development of digital technology, big data is increasingly being applied in various fields. In the health checkup field, big data computing is currently being combined with health checkups. Through digital analysis of user examination results, this data is quantified and disease risk is monitored. However, this approach lacks analysis of user subjective data, resulting in limited personalized monitoring of disease risk information. Summary of the Invention

[0003] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of this application is to propose a disease risk information monitoring method to achieve personalized monitoring of user disease risk information.

[0005] The second purpose of this application is to provide a disease risk information monitoring device.

[0006] The third objective of this application is to provide an electronic device.

[0007] The fourth object of this application is to provide a computer-readable storage medium.

[0008] A fifth object of this application is to provide a computer program product.

[0009] To achieve the above objectives, the first embodiment of the present application proposes a disease risk information monitoring method, comprising:

[0010] Obtaining the user's physical examination indicators, including abnormal physical examination indicators;

[0011] Based on the physical examination index, selecting a target question corresponding to the abnormal physical examination index from a preset question set, wherein any question in the preset question set corresponds to a disease factor;

[0012] Determining a target disease factor of the user based on the feedback received from the user on the target topic;

[0013] The disease risk information of the user is monitored based on the target disease factor.

[0014] To achieve the above objectives, the second embodiment of the present application provides a disease risk information monitoring device, comprising:

[0015] An acquisition module is used to obtain the user's physical examination indicators, including abnormal physical examination indicators;

[0016] a selection module, configured to select, based on the physical examination index, a target question corresponding to the abnormal physical examination index from a preset question set, wherein any question in the preset question set corresponds to a disease factor;

[0017] a determination module, configured to determine the target disease factor of the user based on the feedback result of the user on the target topic received;

[0018] An identification module is used to monitor the disease risk information of the user based on the target disease factor.

[0019] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0020] The memory stores computer-executable instructions;

[0021] The processor executes the computer-executable instructions stored in the memory to implement a disease risk information monitoring method proposed in the embodiment of the first aspect of the present application.

[0022] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by the processor, they are used to implement a disease risk information monitoring method proposed in the first embodiment of the present application.

[0023] To achieve the above-mentioned purpose, the fifth embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements a disease risk information monitoring method proposed in the first embodiment of the present application.

[0024] The disease risk information monitoring method, device, electronic device and storage medium provided by the present application obtain the user's physical examination indicators, including abnormal physical examination indicators; based on the physical examination indicators, select the target topic corresponding to the abnormal physical examination indicator from the preset topic set, wherein any topic in the preset topic set corresponds to a disease factor; determine the user's target disease factor based on the user's feedback on the target topic; and monitor the user's disease risk information based on the target disease factor. This solves the problem of poor personalized monitoring capabilities for user disease risk information. By determining the target topic through physical examination indicators, the user's subjective data can be obtained based on the user's feedback results, the user's physical examination results can be supplemented, and the user's target disease factor can be further determined. The user's disease risk information is personalized monitored, providing a reference for the user's health and increasing the user's attention to their own health.

[0025] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 A flowchart of a disease risk information monitoring method provided in an embodiment of the present application;

[0028] Figure 2 A flowchart of another disease risk information monitoring method provided in an embodiment of the present application;

[0029] Figure 3 A structural diagram of a disease risk information monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes the disease risk information monitoring method and device according to the embodiments of the present application with reference to the accompanying drawings.

[0032] Figure 1 A flow chart of a disease risk information monitoring method provided in an embodiment of the present application.

[0033] In the field of health checkups, the technology currently used to combine big data computing with health checkups and conduct digital analysis of people's health checkup results only analyzes based on the user's health checkup report. This technology has a low level of intelligence and cannot fully grasp the user's health status.

[0034] To address this issue, the present invention provides a disease risk information monitoring method to achieve personalized monitoring of user disease risk information. Figure 1 As shown, the disease risk information monitoring method includes the following steps:

[0035] Step 101: Obtain the user's physical examination indicators, including abnormal physical examination indicators.

[0036] Optionally, physical examination indicators can be obtained by extracting data from the physical examination report, wherein any physical examination indicator corresponds to a normal value range. For example, the routine blood test in the physical examination report includes indicators such as red blood cell count (10^12 / L), hemoglobin (g / L), hematocrit (L / L), and mean corpuscular volume (fL). The normal value range of red blood cell count is 3.8-5.1, the normal value range of hemoglobin is 115-150, the normal value range of hematocrit is 0.35-0.45, and the normal value range of mean corpuscular volume is 80-100. If the value of the physical examination indicator exceeds the normal value range, the corresponding physical examination indicator is confirmed as an abnormal physical examination indicator.

[0037] Step 102 : Based on the physical examination indicators, a target question corresponding to the abnormal physical examination indicator is selected from a preset question set, wherein any question in the preset question set corresponds to a disease factor.

[0038] Optionally, in the case of abnormal physical examination indicators, the user may have health risks and needs to select target questions based on the abnormal physical examination indicators.

[0039] It should be noted that the preset question set includes one or more preset questions corresponding to any abnormal value of a physical examination indicator. The preset question set is stored in the database. When an abnormal physical examination indicator is determined, the corresponding target question is retrieved from the preset question set.

[0040] Optionally, a disease factor is a factor that may cause a disease associated with an abnormal physical examination indicator. For example, if carcinoembryonic antigen is determined to be among the abnormal physical examination indicators, there is a possibility of lung cancer. Corresponding disease factors include smoking, occupational exposure, air pollution, ionizing radiation, diet, genetics, and lung disease history. Accordingly, any question in the preset question set corresponds to a disease factor. For example, the question "Do you smoke?" corresponds to smoking, the question "How often do you smoke?" corresponds to smoking, and the question "Exposure to carcinogens such as radon and asbestos?" corresponds to occupational exposure.

[0041] Step 103: Determine the target disease factor of the user based on the received feedback result of the user on the target topic.

[0042] Optionally, the selected target topic is sent to the user client so that the client can answer the target topic and obtain feedback results. The disease factors are screened based on the received feedback results to determine the target disease factors.

[0043] The feedback result of any target question corresponds to whether the disease factor is established. If the feedback result of the target question is yes, the disease factor corresponding to the target question is established. Conversely, if the feedback result of the target question is no, the disease factor corresponding to the target question is not established. The established disease factor is the target disease factor.

[0044] For example, when it is determined that carcinoembryonic antigen is included in the abnormal physical examination indicators, if the feedback result for the target question "Do you smoke?" is no, then the corresponding disease factor of smoking is not established, and the disease factor of smoking is screened out. If the feedback result for the target question "Whether you are exposed to carcinogens such as radon gas and asbestos," is yes, then the corresponding disease factor of occupational exposure is established, and the disease factor of occupational exposure is determined as the target disease factor.

[0045] Step 104: Monitor the user's disease risk information based on the target disease factor.

[0046] Optionally, big data is called to monitor disease risk information of the user's health status in a certain period of time in the future based on the target disease factor, and the risk levels of related diseases that may be caused by the target disease factor are divided into high risk, medium risk, and low risk, for example, to generate an identification result including the risk level.

[0047] In this embodiment, the user's physical examination indicators are obtained, including abnormal physical examination indicators; based on the physical examination indicators, a target question corresponding to the abnormal physical examination indicator is selected from a preset question set, wherein any question in the preset question set corresponds to a disease factor; based on the user's feedback on the target question, the user's target disease factor is determined; and the user's disease risk information is monitored based on the target disease factor. This solves the problem of poor personalized monitoring capabilities for user disease risk information. By determining the target question through the physical examination indicators, the user's subjective data can be obtained based on the user's feedback results, supplementing the user's physical examination results, further determining the user's target disease factor, combining objective and subjective data, and performing personalized disease risk information monitoring for the user, providing a reference for the user's health and increasing the user's attention to their own health.

[0048] This embodiment provides another disease risk information monitoring method. Figure 2 A flowchart of another disease risk information monitoring method provided in an embodiment of the present application.

[0049] like Figure 2 As shown, the disease risk information monitoring method may include the following steps:

[0050] Step 201: Acquire the user's physical examination indicators, including abnormal physical examination indicators.

[0051] Optionally, any user has a unique user ID, and the same user ID may include one or more physical examination reports of the user. The user's health record can be generated through physical examination reports of multiple physical examinations, and any physical examination report has a unique report ID.

[0052] Optionally, if the same user needs to perform health management for multiple patients, multiple patient IDs can be created under the same user ID. In an embodiment of the present application, a user can create up to 20 patient files, and any patient file corresponds to a unique patient ID.

[0053] Furthermore, a report identifier of any physical examination report corresponding to the user identifier is obtained; the physical examination report corresponding to the report identifier is read and parsed to obtain physical examination indicators.

[0054] Among them, the screening type of the physical examination report can be comprehensive screening or special screening.

[0055] Optionally, read the physical examination report corresponding to any report identifier, parse the physical examination report through data extraction, and obtain the physical examination indicators and corresponding values included in the physical examination report. Any physical examination indicator has a normal value range. When the value of the physical examination indicator exceeds the normal value range, the corresponding physical examination indicator is confirmed as an abnormal physical examination indicator.

[0056] In an embodiment of the present application, after the physical examination report is parsed, the patient identity (such as user ID or patient ID), report identifier (such as report ID) and screening type (such as 1-comprehensive screening, 2-special screening) can be determined. The front end calls the query dynamic questionnaire link interface, and needs to verify that the three parameters of user identifier, report identifier and screening type are not empty, and queries the parsed physical examination indicator list based on the report identifier.

[0057] Step 202: Determine the normal physical examination indicators that successfully match the standard indicators in the physical examination indicators; construct a normal indicator code list based on the normal physical examination indicators; and filter the normal indicator code list in the scale question code list to select the target question.

[0058] Among them, the preset question set corresponds to the scale question coding list, and any standard indicator corresponds to a standard indicator code.

[0059] In the embodiment of the present application, the standard indicators are common physical examination indicators and corresponding standard numerical ranges. Any standard indicator corresponds to a standard indicator code, and the corresponding standard indicator can be located through the standard indicator code. Multiple standard indicators are stored in the database.

[0060] Optionally, the physical examination indicators obtained based on the physical examination report are matched with the standard indicators in the database, the successfully matched physical examination indicators are confirmed as normal physical examination indicators, and the standard indicator codes of each standard indicator corresponding to the normal physical examination indicators are determined to form a normal indicator code list.

[0061] In the embodiment of the present application, the scale question code list is a pre-set standard template of the relationship between questions and options. The normal indicator code list is filtered from the scale question code list, and questions related to normal physical examination indicators are eliminated to determine the target questions.

[0062] Furthermore, the user's gender is determined, and questions that do not match the gender are eliminated from the target questions.

[0063] Optionally, first determine whether the gender is empty. If the gender is empty, query the user information through the user identifier, such as retrieving the user's health file for query, to determine the user's gender.

[0064] For example, if the gender is determined to be male, female-specific questions, such as questions about the menstrual cycle, will be asked in the target questions.

[0065] Furthermore, a questionnaire is generated based on the target topic and displayed on the user interface.

[0066] After filtering out the normal indicator coding list from the scale question coding list, a questionnaire can be generated based on the correspondence between the target question's title and options, and displayed on the user interface, such as in the form of a pop-up window, for the user to fill in.

[0067] In the embodiment of the present application, the core parameters for constructing the questionnaire include tenant ID, appId, scale ID, scale question code list, and script source URL (sourceUrl).

[0068] The tenant ID is a unique ID assigned to the business system by the content platform. Callers corresponding to different business systems share the same content platform, and the tenant ID is used to determine the caller who created the questionnaire. appId is a parameter provided by the content platform and used to send requests. The scale ID is the identifier of the scale question code list. The scale question code list is a unique preset template, and the corresponding scale ID is also unique. sourceUrl is the parameter required to jump to the page after the questionnaire is completed, such as the screening ID or user ID.

[0069] Furthermore, a questionnaire link is generated based on the core parameters and displayed on the user page.

[0070] Step 203: Determine the target disease factor of the user based on the received feedback result of the user on the target topic.

[0071] Optionally, after the questionnaire is displayed on the user interface, the user's feedback results on the target questions are determined by the options clicked by the user, and the feedback results are stored in a database.

[0072] In an embodiment of the present application, after receiving the message queue (MQ) of the user feedback results, the user feedback results are first filtered based on the tenant ID and questionnaire status to determine whether the received user feedback results are feedback on the questionnaire created by the caller. If the condition is not met, it is returned.

[0073] Furthermore, when the received user feedback result is feedback on a questionnaire created by the caller, a screening record for the user is generated, which includes the user ID and report ID of the user; based on the report ID included in the screening record, the target disease factor corresponding to the feedback result is queried.

[0074] In an embodiment of the present application, a logical check is performed on the screening record. When the screening record is empty or the status of the screening record is screened, the physical examination report corresponding to the report identifier cannot be queried, and a result indicating that the query cannot be made is returned, such as the display word "query failed" is returned to the user interface.

[0075] Optionally, any question in the preset question set corresponds to a disease factor, which is derived from indicator data in the physical examination report and data collected from the questionnaire. If logical verification is passed, the user's clicked option in the user feedback results can be used to determine whether the corresponding disease factor is valid, and valid disease factors can be selected as target disease factors.

[0076] The feedback result of any target question corresponds to whether the disease factor is established. If the feedback result of the target question is yes, the disease factor corresponding to the target question is established. Conversely, if the feedback result of the target question is no, the disease factor corresponding to the target question is not established. The established disease factor is the target disease factor.

[0077] For example, when it is determined that carcinoembryonic antigen is included in the abnormal physical examination indicators, if the feedback result for the target question "Do you smoke?" is no, then the corresponding disease factor of smoking is not established, and the disease factor of smoking is screened out. If the feedback result for the target question "Whether you are exposed to carcinogens such as radon gas and asbestos," is yes, then the corresponding disease factor of occupational exposure is established, and the disease factor of occupational exposure is determined as the target disease factor.

[0078] Furthermore, the target disease factors can be deduplicated to reduce the amount of computation during data processing.

[0079] Step 204: Monitor the user's disease risk information based on the target disease factor.

[0080] Optionally, whether disease risk information monitoring of the user can be performed is determined based on the screening records.

[0081] In an embodiment of the present application, a logical check is performed on the screening record. If the screening record is empty or the status of the screening record is screened, the user's disease risk information cannot be monitored; if the user's disease risk information cannot be monitored, an unidentifiable disease risk factor is returned.

[0082] Among them, disease risk factors are factors that make it impossible to monitor disease risk information. When it is impossible to monitor the disease risk information of the user, the disease risk factors will be returned and displayed to the user. For example, the display words "3 risks cannot be assessed" and a click mark will be displayed. After the user clicks the click mark, the user will be redirected to the page displaying the disease risk factors.

[0083] If the logical verification is passed, the disease risk information of the user can be monitored; if the disease risk information of the user can be monitored, the risk level and risk value can be obtained based on the target disease factor.

[0084] In this embodiment of the present application, the input parameters of the big data model are constructed by using a list of physical examination indicators and a list of disease factors: the list of disease factors, the questionnaire ID, and the batch ID. The list of disease factors consists of the English name of the disease factor, its value, its unit, and an enumerated abnormal value outside the normal range.

[0085] Furthermore, it is determined whether the gender and age in the report data are empty. If they are empty, the patient information in the health record is obtained through the user ID.

[0086] In an embodiment of the present application, the disease risk information of the user is monitored by calling the constructed big data model, and high, medium and low risks and corresponding risk values are distinguished.

[0087] Because different disease factors lead to different disease risks, different big data models are called. As an example, when the target disease factor includes smoking, the Logistic Regression model is called to identify the user's coronary heart disease risk. The identification process is as follows:

[0088] Determine the elements corresponding to coronary heart disease risk identification: age, gender, systolic blood pressure and smoking, and set a basic risk reference value for each element. For example, divide age into 10-year intervals and set a basic risk reference value of (30+39) / 2=34.5 based on the age range of 30-39 years old; set the basic risk reference value of females to 0, and the value of males to 1; divide systolic blood pressure into 10mmHg intervals and set the basic risk reference value based on the systolic blood pressure range of 120-129mmHg in, Indicates rounding up; if non-smoking is set as the basic risk reference value of 0, the value of smoking is 1.

[0089] Based on the user's physical examination index list and health records, the user's age, gender and systolic blood pressure are read, and the distance between each element and the basic risk reference value is calculated respectively. The distance between each element and the basic risk reference value can be calculated by the following formula: D i =(W i -W i0 )*β i , where D i Indicates the distance of the i-th element, W i Represents the value of the i-th element, W i0 represents the basic risk reference value of the i-th element, β i Represents the regression coefficient of the i-th element.

[0090] Among them, the regression coefficient can be solved by optimization algorithms such as gradient descent method.

[0091] In the embodiment of the present application, the regression coefficient of age is 0.0575, the regression coefficient of gender is 1.3078, the regression coefficient of systolic blood pressure is 0.0185, and the regression coefficient of smoking is 0.9456.

[0092] Set the scoring tool constant B to score the distance of each element. For example, you can set the risk score to increase by 1 for every 5 years of age increase. Then the scoring tool constant B for age is 5*β i =0.2875, and the score of each element is calculated using the following formula: Among them, P i Represents the score of the i-th element.

[0093] Furthermore, the scores of each element are summed up to obtain the total score P, and the risk value is calculated based on the equation of the logistic regression model, where the equation of the logistic regression model is:

[0094]

[0095] in,

[0096] X=C+γ i *W i0 +V*P

[0097] in, represents the risk value, exp() represents the exponential function with the natural constant e as the base, and C represents a constant.

[0098] The constant C is determined according to the actual processing process.

[0099] It should be noted that the range of risk values in the embodiment of the present application is defined as (0, 100). In other embodiments, the range of risk values can be defined according to actual needs. For example, the equation of the regression model can be modified to So that the risk value range is defined as (0,10).

[0100] Furthermore, the risk levels of different diseases are determined according to the size of the risk values. For example, the risk levels can be divided by setting risk thresholds.

[0101] Optionally, a comprehensive risk value and a comprehensive risk level are determined based on the risk value of each disease.

[0102] For example, weights are preset for different diseases, the risk values of each disease are weighted and summed to obtain the final risk value, and the final risk level is determined based on the preset risk threshold.

[0103] As an example, the final identification result may be that the comprehensive risk is low, the comprehensive risk value is 72 points, there are 12 total risks, 6 high risks, 2 medium risks, and 2 low risks, including high risk of thyroid cancer and medium risk of hypertension.

[0104] Furthermore, corresponding improvement suggestions and service recommendations can be generated in the recognition results, so that users can conduct further special physical examinations according to their own conditions or purchase services such as Internet consultations and family doctors to protect their own health. At the same time, the screening process is closed and conversion is achieved.

[0105] Furthermore, the screening record also includes the screening status. If the disease risk of the user can be identified, after the identification result is generated, the screening status of the screening record is updated to screened, and the screening record and identification result are cached; if the disease risk of the user cannot be identified, the screening status of the screening record is updated to screening failure, and the screening record is cached.

[0106] Among them, the screening status can be to be screened or screened. When the screening status is updated to screened, the screening record has been parsed and cannot be screened again. When performing logical verification on the screening record, if the screening status is screened, the physical examination report corresponding to the report identifier cannot be queried.

[0107] Furthermore, the corresponding disease factor data in the case of risk identification will be stored in the database to update the disease factor database and format the data generated by the questionnaire options.

[0108] In this embodiment, the user's physical examination indicators are obtained, including abnormal physical examination indicators; among the physical examination indicators, normal physical examination indicators that successfully match the standard indicators are determined; a normal indicator code list is constructed based on the normal physical examination indicators; in the scale question code list, the normal indicator code list is filtered to select the target question; based on the user's feedback on the target question, the user's target disease factor is determined; and the user's disease risk information is monitored based on the target disease factor. This solves the problem of poor personalized monitoring capabilities for user disease risk information. By determining the target question through physical examination indicators, the user's subjective data can be obtained based on the user's feedback results, supplementing the user's physical examination results, further determining the user's target disease factor, combining objective data with subjective data, and performing personalized disease risk information monitoring on the user, providing a reference for the user's health and increasing the user's attention to their own health.

[0109] In order to implement the above embodiments, the present application also proposes a disease risk information monitoring device.

[0110] Figure 3A structural diagram of a disease risk information monitoring device provided in an embodiment of the present application.

[0111] like Figure 3 As shown, the disease risk information monitoring device includes: an acquisition module 301, a selection module 302, a determination module 303 and an identification module 304.

[0112] An acquisition module 301 is used to acquire the user's physical examination indicators, including abnormal physical examination indicators;

[0113] A selection module 302 is configured to select a target question corresponding to the abnormal physical examination indicator from a preset question set based on the physical examination indicator, wherein any question in the preset question set corresponds to a disease factor;

[0114] Determination module 303, for determining the user's target disease factor based on the received user's feedback on the target topic;

[0115] The identification module 304 is used to monitor the disease risk information of the user based on the target disease factor.

[0116] Furthermore, in a possible implementation of the embodiment of the present application, the selection module 302 is specifically configured to:

[0117] Among the physical examination indicators, determine the normal physical examination indicators that successfully match the standard indicators;

[0118] A normal indicator coding list is constructed based on normal physical examination indicators, wherein any standard indicator corresponds to a standard indicator code;

[0119] In the scale question code list, filter the normal indicator code list to select the target question, where the preset question set corresponds to the scale question code list.

[0120] Furthermore, in a possible implementation of the embodiment of the present application, the acquisition module 301 is specifically configured to:

[0121] Obtain the report ID of any physical examination report corresponding to the user ID;

[0122] Read the physical examination report corresponding to the report identifier and parse it to obtain the physical examination indicators.

[0123] Furthermore, in a possible implementation of the embodiment of the present application, the disease risk information monitoring device is further configured to:

[0124] Generate a questionnaire based on the target topic and display the questionnaire on the user interface.

[0125] Furthermore, in a possible implementation of the embodiment of the present application, the determination module 303 is specifically configured to:

[0126] Generate a screening record for the user, the screening record including a user ID and a report ID of the user;

[0127] Based on the report identifier included in the screening record, the target disease factor corresponding to the feedback result is queried, wherein the feedback result of any target question corresponds to a relationship pair of whether the disease factor is established.

[0128] Furthermore, in a possible implementation of the embodiment of the present application, the identification module 304 is specifically configured to:

[0129] Determine whether the user's disease risk can be identified based on the screening records;

[0130] When the disease risk of the user can be identified, the risk level and risk value are obtained based on the target disease factor;

[0131] If the disease risk cannot be identified for the user, the disease risk factor that caused the unidentification is returned.

[0132] Perform logical verification on the screening records. If the screening record is empty or the status of the screening record is screened, the user's disease risk cannot be identified.

[0133] Furthermore, in a possible implementation of the embodiment of the present application, the screening record also includes the screening status, and the disease risk identification device is further configured to:

[0134] In the case where the disease risk of the user can be identified, after the identification result is generated, the screening status of the screening record is updated to screened, and the screening record and identification result are cached;

[0135] In the event that the disease risk of the user cannot be identified, the screening status of the screening record is updated to screening failure, and the screening record is cached.

[0136] Determine the user's gender;

[0137] Eliminate gender-inappropriate questions from the target questions.

[0138] It should be noted that the aforementioned explanation of the embodiment of the disease risk information monitoring method is also applicable to the disease risk information monitoring device of this embodiment and will not be repeated here.

[0139] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the disease risk information monitoring method provided in the above embodiments.

[0140] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the disease risk information monitoring method provided in the above embodiments.

[0141] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which, when executed by a processor, implements the disease risk information monitoring method provided in the above embodiments.

[0142] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0143] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0144] This application contemplates providing implementation options for users to selectively block the use or access of personal information data. Specifically, this application contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0145] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0146] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0147] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0148] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0149] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0150] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0151] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0152] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A disease risk information monitoring method, characterized in that: The following steps are involved: Obtaining the user's physical examination indicators, including abnormal physical examination indicators; Based on the physical examination index, selecting a target question corresponding to the abnormal physical examination index from a preset question set, wherein any question in the preset question set corresponds to a disease factor; Determining a target disease factor of the user based on the feedback received from the user on the target topic; The disease risk information of the user is monitored based on the target disease factor.

2. The disease risk information monitoring method according to claim 1, characterized in that: The step of selecting a target topic corresponding to the abnormal physical examination indicator from a preset topic set based on the physical examination indicator includes: Among the physical examination indicators, determining normal physical examination indicators that successfully match the standard indicators; Constructing a normal indicator code list based on the normal physical examination indicators, wherein any standard indicator corresponds to a standard indicator code; In the scale question code list, the normal indicator code list is filtered to select the target question, wherein the preset question set corresponds to the scale question code list.

3. The disease risk information monitoring method according to claim 2, characterized in that: The user has a unique user ID, and obtaining the user's physical examination indicators includes: Obtaining a report ID of any physical examination report corresponding to the user ID; The physical examination report corresponding to the report identifier is read and analyzed to obtain the physical examination indicators.

4. The disease risk information monitoring method according to claim 3, characterized in that: The method further comprises: A questionnaire is generated based on the target topic, and the questionnaire is displayed on a user interface.

5. The disease risk information monitoring method according to claim 3, characterized in that: Determining the target disease factor of the user based on the received feedback result of the user on the target topic includes: generating a screening record for the user, the screening record including a user identifier of the user and the report identifier; Based on the report identifier included in the screening record, the target disease factor corresponding to the feedback result is queried, wherein the feedback result of any target question corresponds to a relationship pair of whether the disease factor is established.

6. The disease risk information monitoring method according to claim 5, characterized in that: The monitoring of the disease risk information of the user based on the target disease factor includes: Determining whether disease risk identification can be performed on the user based on the screening record; When the disease risk of the user can be identified, obtaining a risk level and a risk value based on the target disease factor; In the case that the disease risk of the user cannot be identified, the disease risk factor that caused the inability to identify is returned.

7. The disease risk information monitoring method according to claim 6, characterized in that: The determining whether disease risk identification can be performed on the user based on the screening record includes: A logical check is performed on the screening record. If the screening record is empty or the status of the screening record is "screened", the disease risk of the user cannot be identified.

8. The disease risk information monitoring method according to claim 6, characterized in that: The screening record also includes a screening status, and the method further includes: In a case where the disease risk of the user can be identified, after generating the identification result, updating the screening status of the screening record to screened, and caching the screening record and the identification result; In the case where the disease risk of the user cannot be identified, the screening status of the screening record is updated to screening failure, and the screening record is cached.

9. The disease risk information monitoring method according to claim 1, characterized in that: The method further comprises: determining the gender of the user; Eliminate the target questions that do not match the gender.

10. A disease risk information monitoring device, characterized in that: include: An acquisition module is used to obtain the user's physical examination indicators, including abnormal physical examination indicators; a selection module, configured to select, based on the physical examination index, a target question corresponding to the abnormal physical examination index from a preset question set, wherein any question in the preset question set corresponds to a disease factor; a determination module, configured to determine the target disease factor of the user based on the feedback result of the user on the target topic received; An identification module is used to monitor the disease risk information of the user based on the target disease factor.

11. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 9 when executed by a processor.

13. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 9 when being executed by a processor.

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