CN113658704A - Diabetes risk prediction device, device and storage medium - Google Patents

Abstract

Translated fromChinese

本申请涉及数字医疗领域，根据目标用户的风险测评信息进行糖尿病风险等级预测，可以便捷地检测目标用户患糖尿病的风险。涉及一种糖尿病风险预测设备、装置和介质，该设备实现的步骤包括：获取目标用户的风险测评信息，风险测评信息包括个人信息、糖尿病指标信息、健康状况信息以及家族患病史信息；对健康状况信息进行疾病识别，确定目标用户是否患有糖尿病；若目标用户未患有糖尿病，根据糖尿病指标信息确定目标用户是否为疑似糖尿病患者；若目标用户为非疑似糖尿病患者，根据个人信息、糖尿病指标信息以及家族患病史信息进行糖尿病风险等级预测，获得目标用户患糖尿病的风险等级。此外，本申请还涉及区块链技术，风险测评信息可存储于区块链中。

The present application relates to the field of digital medicine. The diabetes risk level prediction is performed according to the risk assessment information of the target user, so that the risk of diabetes of the target user can be conveniently detected. It relates to a diabetes risk prediction device, device and medium. The steps implemented by the device include: acquiring risk assessment information of target users, where the risk assessment information includes personal information, diabetes index information, health status information and family disease history information; Status information is used to identify the disease to determine whether the target user has diabetes; if the target user does not have diabetes, determine whether the target user is a suspected diabetic according to the diabetes index information; if the target user is a non-suspected diabetic, according to personal information, diabetes indicators Information and family medical history information to predict the risk level of diabetes, and obtain the risk level of diabetes for the target user. In addition, this application also involves blockchain technology, and risk assessment information can be stored in the blockchain.

Description

Diabetes risk prediction device, apparatus and storage medium

Technical Field

The present application relates to the field of digital medical technology, and more particularly, to a diabetes risk prediction device, apparatus, and storage medium.

Background

With the increase of the aging proportion of the population, the population base of potential chronic diseases is continuously expanded, and the society faces increasingly serious chronic disease challenges. Diabetes is a common chronic disease, and diagnosis of diabetes generally requires professional examination in hospitals to determine, but at the early stage of diabetes, symptoms of diabetes may not be revealed, so that the risk of diabetes is not known clearly, and the patients cannot timely go to the hospitals to examine and receive treatment.

Therefore, how to conveniently detect the risk of diabetes of the user becomes an urgent problem to be solved.

Disclosure of Invention

The application provides a diabetes risk prediction device, a diabetes risk prediction device and a storage medium, and the diabetes risk grade prediction is carried out according to personal information, diabetes index information, health condition information and family disease history information of a target user, so that the prediction process is convenient and fast, professional medical equipment is not needed for detection, and the diabetes risk of the target user can be conveniently detected.

In a first aspect, the present application provides a diabetes risk prediction device comprising a memory and a processor;

the memory for storing a computer program;

the processor is configured to execute the computer program and to implement the following steps when executing the computer program:

acquiring risk evaluation information of a target user, wherein the risk evaluation information comprises personal information, diabetes index information, health condition information and family disease history information of the target user;

performing disease identification on the health condition information, and determining whether the target user has diabetes;

if the target user does not have diabetes, determining whether the target user is a suspected diabetic patient according to the diabetes index information;

and if the target user is a non-suspected diabetic patient, predicting the diabetes risk level according to the personal information, the diabetes index information and the family illness history information to obtain the diabetes risk level of the target user.

In a second aspect, the present application also provides a diabetes risk prediction device, the device comprising:

the system comprises an information acquisition module, a risk assessment module and a risk assessment module, wherein the information acquisition module is used for acquiring risk assessment information of a target user, and the risk assessment information comprises personal information, diabetes index information, health condition information and family disease history information of the target user;

the first judgment module is used for carrying out disease identification on the health condition information and determining whether the target user has diabetes;

the second judgment module is used for determining whether the target user is a suspected diabetic patient according to the diabetes index information if the target user does not suffer from diabetes;

and the risk grade prediction module is used for predicting the diabetes risk grade according to the personal information, the diabetes index information and the family disease history information to obtain the risk grade of the target user suffering from diabetes if the target user is a non-suspected diabetic patient.

In a third aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of diabetes risk prediction as described above.

The application discloses a diabetes risk prediction device, a diabetes risk prediction device and a storage medium, wherein personal information, diabetes index information, health condition information and family disease history information of a target user can be obtained by obtaining risk evaluation information of the target user; the disease identification is carried out on the health condition information to determine whether the target user has diabetes, so that the diabetes risk prediction of a patient diagnosed with diabetes can be avoided; when the target user is determined not to have diabetes, whether the target user is a suspected diabetic patient is determined according to the diabetes index information, so that the target user is excluded from being the suspected diabetic patient when the diabetes risk prediction is carried out, and the interference on the prediction result is avoided; when the target user is determined to be a non-suspected diabetic patient, the diabetes risk level is predicted according to the personal information, the diabetes index information and the family disease history information, the risk that the target user suffers from diabetes can be detected conveniently, the prediction process is convenient and fast, and professional medical equipment is not needed for detection.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic block diagram of a diabetes risk prediction apparatus provided in an embodiment of the present application;

FIG. 2 is a schematic flow chart of a diabetes risk prediction step provided by an embodiment of the present application;

FIG. 3 is a schematic illustration of a disease or symptom page provided by an embodiment of the present application;

FIG. 4 is a schematic flow chart diagram of sub-steps of a diabetes risk level prediction provided by an embodiment of the present application;

fig. 5 is a schematic block diagram of a diabetes risk prediction apparatus provided in an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Embodiments of the present application provide a diabetes risk prediction apparatus, a device and a storage medium. The diabetes risk prediction equipment can be included in a server or a terminal, the diabetes risk grade prediction is realized according to the personal information, the diabetes index information, the health condition information and the family disease history information of the target user, the prediction process is convenient and quick, professional medical equipment is not needed for detection, and the diabetes risk of the target user can be detected conveniently.

The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like. The terminal can be an electronic device such as a smart phone, a tablet computer, a notebook computer, a desktop computer and the like.

Referring to fig. 1, fig. 1 is a schematic block diagram illustrating a diabetes risk prediction apparatus according to an embodiment of the present disclosure. As shown in fig. 1, the diabetes risk prediction apparatus includes a processor and a memory connected by a system bus, wherein the memory may include a storage medium and an internal memory. The storage medium includes both nonvolatile storage media and volatile storage media.

The processor is used to provide computational and control capabilities to support the operation of the overall diabetes risk prediction device.

The internal memory provides an environment for the execution of a computer program on a non-volatile storage medium, which when executed by the processor, causes the processor to perform any of the steps of diabetes risk prediction.

It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Referring to fig. 2, fig. 2 is a schematic flow chart illustrating a diabetes risk prediction procedure according to an embodiment of the present application. As shown in fig. 2, the processor is configured to run the computer program stored in the memory to implement the following steps S10 to S40.

Step S10, acquiring risk evaluation information of a target user, wherein the risk evaluation information comprises personal information, diabetes index information, health condition information and family disease history information of the target user.

In some embodiments, when the diabetes risk prediction device is a terminal, the terminal may collect risk assessment information filled in by a target user in an application program of the terminal. The application program may be a preset health management APP or a health management applet, or the like.

For example, the target user may log in a health management APP, and fill in and submit risk assessment information according to actual conditions in the health management APP; and when the health management APP detects the risk evaluation information submitted by the target user, the risk evaluation information is stored in a local database or a local disk.

For another example, the target user may also log in a health management applet, fill in the risk assessment information in the health management applet and click and submit the risk assessment information, and when the health management applet detects the risk assessment information submitted by the target user, store the risk assessment information in a local database or a local disk.

In other embodiments, when the diabetes risk prediction device is a server, the server may receive the risk assessment information uploaded by the terminal. The risk assessment information can be filled in by the target user in a health management APP or a health management applet of the terminal. For example, when detecting the risk assessment information submitted by the target user, the terminal uploads the risk assessment information to the server.

For example, the risk assessment information may include personal information, diabetes index information, health status information, and family history information of the target user.

The personal information may include, but is not limited to, name, gender, contact information, age, height, weight, and waist circumference.

The diabetes index information may include a fasting blood glucose value and a postprandial blood glucose value. The target user can detect the fasting blood glucose level and the postprandial blood glucose level by the blood glucose meter. In addition, the diabetes index information may further include glycated hemoglobin.

The health condition information refers to information such as a disease or a symptom currently suffered by the target user. Referring to fig. 3, fig. 3 is a schematic view of a disease or symptom page according to an embodiment of the present disclosure. As shown in fig. 3, the determination may be made based on the selected operation of the target user on the disease or symptom page on the health management APP or health management applet.

Illustratively, the family history information refers to information on whether a person having a blood relationship with a target user has a disease such as diabetes, hypertension, and chronic kidney disease. For example, the target user may select from a family medical history survey page of a health management APP or health management applet depending on the situation.

In order to further ensure the privacy and security of the risk assessment information, the risk assessment information may be stored in a node of a block chain. And reading the risk evaluation information of the target user from the block chain node.

By acquiring the risk evaluation information of the target user, the personal information, the diabetes index information, the health condition information and the family disease history information of the target user can be acquired, and the diabetes risk prediction can be subsequently performed according to the personal information, the diabetes index information, the health condition information and the family disease history information.

And step S20, performing disease identification on the health condition information, and determining whether the target user has diabetes.

For example, the health condition information may be subjected to disease identification, and if a disease "diabetes" exists in the health condition information, the target user may be determined to be a diabetes diagnosed user. At this time, the evaluation result of the existing diabetes can be output on the direct health management APP or the health management applet without the need of performing the diabetes risk prediction.

It should be noted that, by performing disease identification on the health condition information, it is determined whether the target user has diabetes, so that the diabetes risk prediction for a patient diagnosed with diabetes can be avoided. It is to be understood that in the embodiments of the present application, the diabetes risk prediction is only performed for users who do not have diabetes or patients who are not suspected to have diabetes.

And step S30, if the target user does not have diabetes, determining whether the target user is suspected diabetic according to the diabetes index information.

It should be noted that, after determining that the target user does not have diabetes, it may be further determined whether the target user is a suspected diabetic patient. When the target user is determined not to have diabetes, whether the target user is suspected diabetic is determined according to the diabetes index information, so that the target user is excluded from being suspected diabetic when the diabetes risk prediction is carried out, and the interference on the prediction result is avoided.

For example, whether the target user is suspected diabetic may be determined according to the diabetes index information. For example, it is possible to determine whether or not the target user is a suspected diabetic patient based on the fasting blood glucose level and/or the postprandial blood glucose level in the diabetes index information.

In some embodiments, the target user is determined to be a suspected diabetic patient if the fasting glucose value is greater than or equal to the first glucose threshold and/or the post-prandial glucose value is greater than or equal to the second glucose threshold. And if the fasting blood glucose value is smaller than the first blood glucose threshold value and the postprandial blood glucose value is smaller than the second blood glucose threshold value, determining that the target user is a non-suspected diabetic patient.

The first blood glucose threshold and the second blood glucose threshold may be set according to actual conditions, and specific values are not limited herein. For example, the first glycemic threshold may be 7.0 mmol/L; the second glycemic threshold may be 11.1 mmol/L.

For example, when the fasting blood glucose value is greater than or equal to 7.0mol/L and/or the postprandial blood glucose value is greater than or equal to 11.1mmol/L, the target user may be determined to be a suspected diabetic patient. When the fasting blood glucose value is less than 7.0mol/L and the postprandial blood glucose value is less than 11.1mmol/L, the target user can be determined to be a non-suspected diabetic patient. The postprandial blood glucose level may be a blood glucose level measured within 1 hour or 2 hours after a meal by the target user.

Whether the target user is a suspected diabetic patient can be conveniently and accurately judged according to the fasting blood glucose value and/or the postprandial blood glucose value.

And step S40, if the target user is a non-suspected diabetic patient, performing diabetes risk level prediction according to the personal information, the diabetes index information and the family illness history information to obtain a risk level of the target user suffering from diabetes.

In the embodiment of the present application, after determining that the target user is a non-suspected diabetic patient, the diabetes risk level may be predicted according to the personal information, the diabetes index information, and the family disease history information, so as to obtain the risk level of the target user suffering from diabetes. When the target user is determined to be a non-suspected diabetic patient, the diabetes risk level is predicted according to the personal information, the diabetes index information and the family disease history information, the risk that the target user suffers from diabetes can be detected conveniently, the prediction process is convenient and fast, and professional medical equipment is not needed for detection.

Referring to fig. 4, fig. 4 is a schematic flow chart illustrating sub-steps of diabetes risk level prediction according to an embodiment of the present application, which may specifically include the following steps S401 to S403.

Step S401, determining the body quality index of the target user according to the personal information.

For example, the personal information may include a height value and a weight value.

In some embodiments, determining the body mass index of the target user from the personal information may include: extracting a height value and a weight value in the personal information; and calculating the height value and the weight value based on a body mass index calculation formula to obtain a body mass index corresponding to the target user.

It should be noted that the body Mass index may be BMI (body Mass index). The BMI index is calculated as: body Mass Index (BMI) is the square of weight (kg) ÷ height (m).

For example, the height value and the weight value may be calculated according to the body mass index calculation formula, so as to obtain the body mass index corresponding to the target user.

The height value and the weight value are calculated based on the body mass index calculation formula, the body mass index corresponding to the target user is obtained, the body mass index can be subsequently used as one of factors influencing diabetes to predict the diabetes risk, and the accuracy of the diabetes risk prediction can be further improved.

And S402, based on a preset risk prediction strategy, performing diabetes risk prediction according to the body quality index, the diabetes index information and the family illness history information to obtain a diabetes risk prediction score corresponding to the target user.

It should be noted that, in the embodiment of the present application, the diabetes risk prediction is comprehensively performed according to the body quality index, the diabetes index information, and the family disease history information, the prediction process is convenient and fast, professional medical equipment is not required for detection, and the risk of the target user suffering from diabetes can be detected conveniently and accurately.

In some embodiments, based on a preset risk prediction policy, performing diabetes risk prediction according to the body mass index, the diabetes index information, and the family disease history information, and obtaining a diabetes risk prediction score corresponding to the target user may include: respectively determining a first diabetes risk score corresponding to the body mass index, a second diabetes risk score corresponding to the diabetes index information and a third diabetes risk score corresponding to the family disease history information; and adding the first diabetes risk score, the second diabetes risk score and the third diabetes risk score to obtain a diabetes risk prediction score.

For example, determining a first diabetes risk score corresponding to the body mass index may include: when the body quality index is greater than or equal to the body quality index threshold, determining a score value corresponding to the body quality index according to the difference between the body quality index and the body quality index threshold; and multiplying the score value corresponding to the body mass index by the weight value corresponding to the body mass index to obtain a first diabetes risk score. When the body mass index is less than the body mass index threshold, the first diabetes risk score may be set to 0.

The body mass index threshold may be set according to actual conditions, and the specific value is not limited herein.

In the case of specifying the second diabetes risk score corresponding to the diabetes index information, since the diabetes index information may include a fasting blood glucose value, a postprandial blood glucose value, and a glycated hemoglobin value, the second diabetes risk wind value may be specified from one or more of the fasting blood glucose value, the postprandial blood glucose value, and the glycated hemoglobin value.

In some embodiments, the second diabetes risk profile may be determined from a fasting blood glucose value. For example, when the fasting blood glucose value is greater than or equal to the fasting blood glucose threshold, determining a score value corresponding to the fasting blood glucose value according to the difference between the fasting blood glucose value and the fasting blood glucose threshold; and multiplying the score value corresponding to the fasting blood glucose value by the weight value corresponding to the fasting blood glucose value to obtain a second diabetes risk wind value. The second diabetes risk profile may be set to 0 when the fasting glucose value is less than the fasting glucose threshold.

The fasting blood glucose threshold may be set according to actual conditions, and the specific value is not limited herein.

In other embodiments, a second diabetes risk profile may be determined based on the fasting blood glucose value, the post-prandial blood glucose value, and the glycated hemoglobin value. Exemplarily, a first score value corresponding to a fasting blood glucose value, a second score value corresponding to a postprandial blood glucose value and a third score value corresponding to a glycated hemoglobin value are determined; and taking a mean value of the first score value, the second score value and the third score value, and multiplying the mean value by a preset weight value to obtain a second diabetes risk wind value.

For example, determining a first score value corresponding to a fasting blood glucose value may include: when the fasting blood glucose value is greater than or equal to the fasting blood glucose threshold value, determining a first score value according to the difference between the fasting blood glucose value and the fasting blood glucose threshold value; when the fasting glucose value is less than the fasting glucose threshold, the first score value may be determined to be 0. In addition, the specific process of determining the second score value corresponding to the postprandial blood glucose value and the third score value corresponding to the glycated hemoglobin value is similar to the specific process of determining the first score value corresponding to the fasting blood glucose value, and the specific processes are not repeated herein.

For example, determining a third diabetes risk score corresponding to family history information may include: determining the number of the diabetics according to the family illness history information, and taking the number of the diabetics as a score value corresponding to the family illness history information; and multiplying the score value corresponding to the family illness history information by the weight value corresponding to the family illness history information to obtain a third diabetes risk score.

It should be noted that, the weight value corresponding to the body mass index, the weight value corresponding to the diabetes index information, and the weight value corresponding to the family illness history information may be set according to the importance degree that the body mass index, the diabetes index information, and the family illness history information affect diabetes, and the specific numerical value is not limited herein.

According to the method, the first diabetes risk score corresponding to the body quality index, the second diabetes risk score corresponding to the diabetes index information and the third diabetes risk score corresponding to the family disease history information are respectively determined based on the importance degree of the body quality index, the diabetes index information and the family disease history information on the diabetes, and the accuracy of diabetes risk prediction is improved.

And S403, determining the risk level of the target user suffering from diabetes according to the diabetes risk prediction score.

In some embodiments, determining a level of risk of the target user for diabetes based on the diabetes risk prediction score may include: determining a target value range corresponding to the diabetes risk prediction value; and determining the risk level of the target user suffering from diabetes according to the target score range based on the corresponding relation between the preset score range and the risk level.

It should be noted that, the corresponding relationship between the different score ranges and the diabetes risk levels can be preset. For example, the risk levels may include a high risk level, a medium risk level, and a low risk level; the score range may include a first score range, a second score range, and a third score range. The first fractional range, the second fractional range and the third fractional range may be divided according to actual situations, and specific numerical values are not limited herein. For example, a first range of scores corresponds to a high risk level, a second range of scores corresponds to a medium risk level, and a third range of scores corresponds to a low risk level.

For example, if it is determined that the target score range corresponding to the diabetes risk prediction score is the first score range, the risk level of the target user suffering from diabetes may be determined to be a high risk level.

For example, if the target score range corresponding to the diabetes risk prediction score is determined to be the third score range, the risk level of the target user suffering from diabetes may be determined to be a low risk level.

In some embodiments, after obtaining the risk level of the target user suffering from diabetes, the method may further include: and outputting the risk level of the target user suffering from diabetes.

For example, when the diabetes risk prediction apparatus is a server, the server may transmit the risk level to the terminal to display the risk level of the target user suffering from diabetes on the terminal. For example, the level of risk of the target user to develop diabetes may be displayed in a health management APP or health management applet. For another example, the risk level may be sent to the terminal of the target user or the terminal of the emergency contact of the target user by means of short message, email, instant message, and the like.

For example, when the diabetes risk prediction device is a terminal of the target user, the terminal may display the risk level of the target user suffering from diabetes in the health management APP or the health management applet.

By outputting the risk level of the target user suffering from diabetes, the target user can be reminded to timely go to a hospital for examination and receiving treatment, and the condition of illness is prevented from being delayed.

In some embodiments, after obtaining the risk level of the target user suffering from diabetes, the method may further include: and when the target user meets the preset condition, acquiring a diabetes control scheme corresponding to the target user, and outputting the diabetes control scheme. Wherein the target user meeting the preset condition comprises at least one of the following: the target user has diabetes; the target user is a suspected diabetic patient; the risk level of the target user suffering from diabetes is a preset risk level.

For example, the preset risk level may be a high risk level or a medium risk level.

In some embodiments, determining the diabetes control profile for the target user may include: acquiring diabetes detection information of a target user; and matching the diabetes control scheme according to the diabetes detection information to obtain the diabetes control scheme corresponding to the target user.

Illustratively, the method can be networked with a medical platform to acquire the diabetes detection information of a target user. The diabetes inspection information may include, but is not limited to, personal health profile, prescription, inspection report, etc. data of the target user.

For example, the diabetes check information filled in by the target user on the health management APP or the health management applet may also be obtained. Such as diagnostic information and/or physical examination information of the target user, etc. Wherein the diagnostic information may be that the target user has diabetes. Physical examination information may include, but is not limited to, glycated hemoglobin, liver function, blood glucose values, blood lipids, kidney function, electrocardiogram, and hematuria routine, among others.

For example, the diabetes control scheme matching may be performed according to the diabetes inspection information, so as to obtain the diabetes control scheme corresponding to the target user. In the embodiment of the application, the corresponding diabetes control scheme can be matched in advance according to each index in the diabetes detection information. For example, the diabetes control scheme matching may be performed according to the diabetes inspection information to generate an initial diabetes control scheme; and then uploading the initial diabetes control scheme to a medical platform, logging in the medical platform by a professional doctor, adjusting the initial diabetes control scheme, and issuing the adjusted diabetes control scheme to the diabetes risk prediction equipment.

Illustratively, when the diabetes risk prediction device is a server, the doctor may send the adjusted diabetes control scheme to the server through the medical platform, and the server sends the adjusted diabetes control scheme to the terminal of the target user; the doctor can also send the adjusted diabetes control scheme to the terminal of the target user through the medical platform. When the diabetes risk prediction device is the terminal of the target user, the doctor can send the adjusted diabetes control scheme to the terminal of the target user through the medical platform.

For example, the diabetes control regimen may include, but is not limited to, a blood glucose measurement regimen, a diet regimen, an exercise regimen, a follow-up regimen, and a patient education regimen, among others.

Wherein the blood glucose measurement protocol may include: when the target user has used insulin and the glycated hemoglobin value is less than 7%, it is recommended to test three times a week (fasting, after breakfast, after dinner) distributed over 3 days and for 3 consecutive days. When the target user did not use insulin and the glycated hemoglobin value was less than 7%, the first week was monitored 2 times, before breakfast and after breakfast respectively; the second week was monitored 2 times, before and after lunch, respectively; monitoring for 2 times in the third week, before and after dinner respectively; monitoring once in the fourth week and measuring before sleeping; before the return visit, before and after breakfast, after lunch, after dinner and before sleep were monitored. When the target user uses insulin and the glycated hemoglobin value is greater than 8%, the measurement is performed twice a week, one day before breakfast and one day after dinner. When the target user uses insulin and the glycated hemoglobin value is 7% -8%, the detection is performed 4 times a week, namely fasting, after breakfast, after lunch and after dinner, which are distributed for 4 days and continuous for 4 days.

For example, a dietary regimen may include the type and amount of food ingested by the target user per meal. The exercise program may include the type of exercise and exercise time that the target user needs to perform daily, and so on. The education protocol is an article pushed to a target user, and can comprise risk control, diet guidance, exercise guidance, blood sugar monitoring, medication guidance, complications and other types of articles.

The diabetes control scheme of the target user is determined according to the diabetes inspection information and output, so that the target user can control diabetes according to the diabetes control scheme, the target user can obtain better medical service, the doctor-patient relationship is improved, and the problem that a diabetic patient difficultly sees a doctor is solved.

The diabetes risk prediction device provided by the above embodiment can obtain the personal information, the diabetes index information, the health condition information and the family disease history information of the target user by obtaining the risk evaluation information of the target user, and can perform the diabetes risk prediction according to the personal information, the diabetes index information, the health condition information and the family disease history information; whether the target user is a suspected diabetic patient or not can be conveniently and accurately judged according to the fasting blood glucose value and/or the postprandial blood glucose value; the height value and the weight value are calculated based on the body mass index calculation formula to obtain the body mass index corresponding to the target user, and the body mass index can be subsequently used as one of factors influencing diabetes to predict the diabetes risk, so that the accuracy of predicting the diabetes risk can be improved; the method comprises the steps of respectively determining a first diabetes risk score corresponding to the body quality index, a second diabetes risk score corresponding to the diabetes index information and a third diabetes risk score corresponding to the family disease history information based on the importance degree of the body quality index, the diabetes index information and the family disease history information on diabetes, so that the accuracy of diabetes risk prediction is improved; by outputting the risk level of the target user suffering from diabetes, the target user can be reminded to timely go to a hospital for examination and receiving treatment, so that the condition of illness is prevented from being delayed; the diabetes control scheme of the target user is determined according to the diabetes inspection information and output, so that the target user can control diabetes according to the diabetes control scheme, the target user can obtain better medical service, the doctor-patient relationship is improved, and the problem that a diabetic patient difficultly sees a doctor is solved.

Referring to fig. 5, fig. 5 is a schematic block diagram of a diabetesrisk prediction device 1000 according to an embodiment of the present application, which is used for performing the aforementioned diabetes risk prediction steps. The diabetes risk prediction device may be disposed in a server or a terminal.

As shown in fig. 5, the diabetesrisk prediction device 1000 includes: an information acquisition module 1001, afirst judgment module 1002, asecond judgment module 1003 and a risklevel prediction module 1004.

The information acquiring module 1001 is configured to acquire risk assessment information of a target user, where the risk assessment information includes personal information, diabetes index information, health condition information, and family medical history information of the target user.

A first determiningmodule 1002, configured to perform disease identification on the health condition information, and determine whether the target user has diabetes.

A second determiningmodule 1003, configured to determine, if the target user does not have diabetes, whether the target user is a suspected diabetic patient according to the diabetes index information.

A risklevel prediction module 1004, configured to, if the target user is a non-suspected diabetic patient, perform diabetes risk level prediction according to the personal information, the diabetes index information, and the family illness history information, to obtain a risk level of the target user suffering from diabetes.

It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus and the modules described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, where the computer program includes program instructions, and the processor executes the program instructions to implement any one of the steps of diabetes risk prediction provided in the embodiments of the present application.

For example, the program is loaded by a processor and may perform the following steps:

acquiring risk evaluation information of a target user, wherein the risk evaluation information comprises personal information, diabetes index information, health condition information and family disease history information of the target user; performing disease identification on the health condition information, and determining whether the target user has diabetes; if the target user does not have diabetes, determining whether the target user is a suspected diabetic patient according to the diabetes index information; and if the target user is a non-suspected diabetic patient, predicting the diabetes risk level according to the personal information, the diabetes index information and the family illness history information to obtain the diabetes risk level of the target user.

The computer readable storage medium may be an internal storage unit of the diabetes risk prediction device according to the foregoing embodiment, for example, a hard disk or a memory of the diabetes risk prediction device. The computer readable storage medium may also be an external storage device of the diabetes risk prediction device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital Card (SD Card), a Flash memory Card (Flash Card), and the like, which are provided on the diabetes risk prediction device.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

Translated fromChinese

1.一种糖尿病风险预测设备，其特征在于，所述糖尿病风险预测设备包括存储器和处理器；1. A diabetes risk prediction device, characterized in that the diabetes risk prediction device comprises a memory and a processor;所述存储器，用于存储计算机程序；the memory for storing computer programs;所述处理器，用于执行所述计算机程序并在执行所述计算机程序时实现如下步骤：The processor is configured to execute the computer program and implement the following steps when executing the computer program:获取目标用户的风险测评信息，所述风险测评信息包括所述目标用户的个人信息、糖尿病指标信息、健康状况信息以及家族患病史信息；Obtain risk assessment information of the target user, where the risk assessment information includes the target user's personal information, diabetes index information, health status information, and family disease history information;对所述健康状况信息进行疾病识别，确定所述目标用户是否患有糖尿病；Perform disease identification on the health status information to determine whether the target user suffers from diabetes;若所述目标用户未患有糖尿病，则根据所述糖尿病指标信息确定所述目标用户是否为疑似糖尿病患者；If the target user does not suffer from diabetes, determining whether the target user is a suspected diabetic patient according to the diabetes index information;若所述目标用户为非疑似糖尿病患者，则根据所述个人信息、所述糖尿病指标信息以及所述家族患病史信息进行糖尿病风险等级预测，获得所述目标用户患糖尿病的风险等级。If the target user is a non-suspected diabetic patient, predict the diabetes risk level according to the personal information, the diabetes index information and the family medical history information, and obtain the risk level of the target user suffering from diabetes.2.根据权利要求1所述的糖尿病风险预测设备，其特征在于，所述糖尿病指标信息包括空腹血糖值和餐后血糖值；所述处理器实现根据所述糖尿病指标信息确定所述目标用户是否为疑似糖尿病患者的步骤，包括：2 . The diabetes risk prediction device according to claim 1 , wherein the diabetes index information includes a fasting blood glucose value and a postprandial blood glucose value; and the processor determines whether the target user is based on the diabetes index information. 3 . Steps for people with suspected diabetes, including:若所述空腹血糖值大于或等于第一血糖阈值，和/或所述餐后血糖值大于或等于第二血糖阈值，则确定所述目标用户为疑似糖尿病患者。If the fasting blood glucose value is greater than or equal to the first blood glucose threshold, and/or the postprandial blood glucose value is greater than or equal to the second blood glucose threshold, it is determined that the target user is a suspected diabetic patient.3.根据权利要求1所述的糖尿病风险预测设备，其特征在于，所述处理器实现根据所述个人信息、所述糖尿病指标信息以及所述家族患病史信息进行糖尿病风险等级预测，获得所述目标用户患糖尿病的风险等级的步骤，包括：3 . The diabetes risk prediction device according to claim 1 , wherein the processor implements the prediction of diabetes risk level according to the personal information, the diabetes index information and the family disease history information, and obtains the obtained information. 4 . Steps to describe the target user's risk level for diabetes, including:根据所述个人信息，确定所述目标用户的身体质量指数；According to the personal information, determine the body mass index of the target user;基于预设的风险预测策略，根据所述身体质量指数、所述糖尿病指标信息以及所述家族患病史信息进行糖尿病风险预测，获得所述目标用户对应的糖尿病风险预测分值；Based on a preset risk prediction strategy, diabetes risk prediction is performed according to the body mass index, the diabetes index information, and the family medical history information, and a diabetes risk prediction score corresponding to the target user is obtained;根据所述糖尿病风险预测分值，确定所述目标用户患糖尿病的风险等级。According to the diabetes risk prediction score, the risk level of diabetes of the target user is determined.4.根据权利要求3所述的糖尿病风险预测设备，其特征在于，所述处理器实现根据所述个人信息，确定所述目标用户的身体质量指数的步骤，包括：4. The diabetes risk prediction device according to claim 3, wherein the processor implements the step of determining the body mass index of the target user according to the personal information, comprising:提取所述个人信息中的身高值与体重值；extracting the height value and weight value in the personal information;基于身体质量指数计算公式，对所述身高值与所述体重值进行计算，获得所述目标用户对应的身体质量指数。Based on the body mass index calculation formula, the height value and the weight value are calculated to obtain the body mass index corresponding to the target user.5.根据权利要求3所述的糖尿病风险预测设备，其特征在于，所述处理器实现基于预设的风险预测策略，根据所述身体质量指数、所述糖尿病指标信息以及所述家族患病史信息进行糖尿病风险预测，获得所述目标用户对应的糖尿病风险预测分值的步骤，包括：5 . The diabetes risk prediction device according to claim 3 , wherein the processor implements a preset risk prediction strategy based on the body mass index, the diabetes index information and the family disease history. 6 . The steps of obtaining diabetes risk prediction score corresponding to the target user include:分别确定所述身体质量指数对应的第一糖尿病风险分值、所述糖尿病指标信息对应的第二糖尿病风险分值以及所述家族患病史信息对应的第三糖尿病风险分值；respectively determining the first diabetes risk score corresponding to the body mass index, the second diabetes risk score corresponding to the diabetes index information, and the third diabetes risk score corresponding to the family medical history information;将所述第一糖尿病风险分值、所述第二糖尿病风险分值和所述第三糖尿病风险分值相加，获得所述糖尿病风险预测分值。The first diabetes risk score, the second diabetes risk score, and the third diabetes risk score are added to obtain the diabetes risk prediction score.6.根据权利要求3所述的糖尿病风险预测设备，其特征在于，所述处理器实现根据所述糖尿病风险预测分值，确定所述目标用户患糖尿病的风险等级的步骤，包括：6. The diabetes risk prediction device according to claim 3, wherein the processor implements the step of determining the diabetes risk level of the target user according to the diabetes risk prediction score, comprising:确定所述糖尿病风险预测分值对应的目标分值范围；determining the target score range corresponding to the diabetes risk prediction score;基于预设的分值范围与风险等级之间的对应关系，根据所述目标分值范围确定所述目标用户患糖尿病的风险等级。Based on the corresponding relationship between the preset score range and the risk level, the risk level of the target user suffering from diabetes is determined according to the target score range.7.根据权利要求1所述的糖尿病风险预测设备，其特征在于，所述处理器实现的步骤还包括：7. The diabetes risk prediction device according to claim 1, wherein the step implemented by the processor further comprises:当所述目标用户满足预设条件时，获取所述目标用户对应的糖尿病控制方案，并输出所述糖尿病控制方案；When the target user satisfies the preset condition, acquiring the diabetes control plan corresponding to the target user, and outputting the diabetes control plan;所述目标用户满足预设条件包括以下至少一项：The target user satisfying the preset condition includes at least one of the following:所述目标用户患有糖尿病；the target user suffers from diabetes;所述目标用户为疑似糖尿病患者；The target user is a suspected diabetic patient;所述目标用户患糖尿病的风险等级为预设风险等级。The risk level of the target user suffering from diabetes is a preset risk level.8.根据权利要求7所述的糖尿病风险预测设备，其特征在于，所述处理器实现确定所述目标用户对应的糖尿病控制方案的步骤，包括：8. The diabetes risk prediction device according to claim 7, wherein the processor implements the step of determining a diabetes control scheme corresponding to the target user, comprising:获取所述目标用户的糖尿病检查信息；Obtain the diabetes check information of the target user;根据所述糖尿病检查信息进行糖尿病控制方案匹配，获得所述目标用户对应的糖尿病控制方案。A diabetes control plan is matched according to the diabetes check information, and a diabetes control plan corresponding to the target user is obtained.9.一种糖尿病风险预测装置，其特征在于，包括：9. A diabetes risk prediction device, comprising:信息获取模块，用于获取目标用户的风险测评信息，所述风险测评信息包括所述目标用户的个人信息、糖尿病指标信息、健康状况信息以及家族患病史信息；an information acquisition module, configured to acquire risk assessment information of the target user, where the risk assessment information includes the target user's personal information, diabetes index information, health status information and family medical history information;第一判断模块，用于对所述健康状况信息进行疾病识别，确定所述目标用户是否患有糖尿病；a first judgment module, configured to perform disease identification on the health status information, and determine whether the target user suffers from diabetes;第二判断模块，用于若所述目标用户未患有糖尿病，则根据所述糖尿病指标信息确定所述目标用户是否为疑似糖尿病患者；a second judgment module, configured to determine whether the target user is a suspected diabetic patient according to the diabetes index information if the target user does not suffer from diabetes;风险等级预测模块，用于若所述目标用户为非疑似糖尿病患者，则根据所述个人信息、所述糖尿病指标信息以及所述家族患病史信息进行糖尿病风险等级预测，获得所述目标用户患糖尿病的风险等级。The risk level prediction module is used to predict the diabetes risk level according to the personal information, the diabetes index information and the family medical history information if the target user is a non-suspected diabetic patient, and obtain the risk level of the target user. Diabetes risk level.10.一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时使所述处理器实现如权利要求1至8任一项所述糖尿病风险预测的步骤。10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements any one of claims 1 to 8 The steps of diabetes risk prediction.

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