CN113160993A - Health intervention system, server and health management system - Google Patents

Health intervention system, server and health management system Download PDF

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CN113160993A
CN113160993A CN202011633958.6A CN202011633958A CN113160993A CN 113160993 A CN113160993 A CN 113160993A CN 202011633958 A CN202011633958 A CN 202011633958A CN 113160993 A CN113160993 A CN 113160993A
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health
intervention
user
disease
module
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樊代明
钟南山
姚娟娟
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Shanghai Mingping Medical Data Technology Co ltd
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Shanghai Mingping Medical Data Technology Co ltd
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Priority to PCT/CN2021/086310 priority patent/WO2022141927A1/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

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Abstract

The invention provides a health intervention system, a server and a health management system. The health intervention system comprises: the storage module stores a disease risk correlation model; the disease risk association model comprises a plurality of diseases, risk factors and intervention measures; the health file acquisition module is used for acquiring a health file of a user; the related disease acquisition module is used for searching related diseases in the disease risk correlation model according to the health file of the user; the illness probability calculation module is used for calculating the illness probability of the related diseases according to the disease risk correlation model; and the intervention scheme generation module is used for generating an intervention scheme according to the prevalence probability of the related diseases and the intervention measures related to the related diseases. The health intervention system fully considers the probability that the user has the related diseases or possibly has the related diseases in the future, and the generated intervention scheme is high in pertinence.

Description

Health intervention system, server and health management system
Technical Field
The invention belongs to the field of medical care informatics, relates to an intervention system, and particularly relates to a health intervention system, a server and a health management system.
Background
The occurrence, development process and risk factors of diseases, particularly chronic non-infectious diseases, are interventionalisable, which makes healthy interventions possible. The health intervention means that risk factors possibly causing diseases are detected and evaluated systematically to help people perform targeted preventive intervention before the diseases are formed, so that the aims of blocking, delaying or preventing the occurrence and development processes of the diseases and maintaining health are fulfilled. The existing health intervention scheme is based on the health data of the user, and a corresponding intervention plan is directly made; wherein the intervention plan comprises a plurality of intervention measures. In practical applications, the inventor finds that the probability that the user suffers from the disease or may suffer from the disease in the future in real life is different, and therefore, the adopted intervention measures for different disease probability are different. For example, for a hypertensive patient with a 100% prevalence probability, the intervention may include medication, hospital visits, etc., while for a hypertensive patient with a 10% prevalence probability, the intervention may be diet improvement, exercise enhancement, etc. However, no existing health intervention scheme takes such difference of the prevalence probability into account, so that the existing health intervention scheme is poor in pertinence.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to provide a health intervention system, a server and a health management system, which are used for solving the problem that the prior art does not consider the disease variability when making an intervention plan.
To achieve the above and other related objects, a first aspect of the present invention provides a health intervention system; the health intervention system comprises: the storage module stores a disease risk correlation model; the disease risk association model comprises a plurality of diseases, risk factors and intervention measures; wherein each disease is associated with at least one risk factor and at least one intervention; the health file acquisition module is used for acquiring a health file of a user; the health profile of the user comprises profile sub-information of the user, and the profile sub-information of the user is associated with the risk factors; the related disease acquisition module is connected with the storage module and the health file acquisition module and used for searching related diseases from the disease risk correlation model according to the health file of the user; the illness probability calculation module is connected with the related disease acquisition module and the storage module and used for calculating the illness probability of the related disease according to the disease risk correlation model; and the intervention scheme generation module is connected with the morbidity probability calculation module and the storage module and is used for generating an intervention scheme according to the morbidity probability of the related diseases and the intervention measures related to the related diseases.
In an embodiment of the first aspect, the relevant disease obtaining module includes: the risk factor acquisition unit is connected with the health file acquisition module and used for acquiring the risk factors of the user according to the file sub-information of the user; and the related disease searching unit is connected with the risk factor acquiring unit and the storage module and is used for searching the related diseases from the disease risk correlation model according to the risk factors of the user.
In an embodiment of the first aspect, the prevalence probability calculating module includes: the weight calculation unit is connected with the related disease acquisition module and the storage module and is used for calculating the weight value of the risk factor of the user according to the disease risk association model; and the probability calculation unit is connected with the weight calculation unit and the storage module and is used for calculating the disease probability of the related diseases according to the weight value of the risk factor of the user and the disease risk association model.
In an embodiment of the first aspect, the prevalence probability calculating module includes: the training data acquisition unit is connected with the storage module and used for acquiring training data; the training data is derived from the disease risk association model and a diagnostic case database; the neural network training unit is connected with the training data acquisition unit and used for training a neural network model by using the training data to obtain a trained probability calculation neural network model; and the neural network processing unit is connected with the neural network training unit and the related disease acquisition module and is used for processing the risk factors of the user by utilizing the probability calculation neural network model so as to obtain the disease probability of the related disease.
In an embodiment of the first aspect, the intervention scheme generating module includes a habit intervention unit, a drug intervention unit, a hospitalization intervention unit, a knowledge intervention unit and/or a financial intervention unit; the habit intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a life habit intervention scheme according to the morbidity probability of the related diseases, intervention measures related to the related diseases and the health record of the user; the drug intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a drug intervention scheme according to the morbidity probability of the related diseases, intervention measures related to the related diseases and the health record of the user; the hospitalizing intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a hospitalizing intervention scheme according to the morbidity probability of the related diseases, the intervention measures related to the related diseases and the health record of the user; the knowledge intervention unit is connected with the disease probability calculation module, the storage module and the health record acquisition module and is used for generating a knowledge intervention scheme according to the disease probability of the related diseases, the intervention measures related to the related diseases and the health record of the user; the financial intervention unit is connected with the disease probability calculation module, the storage module and the health record acquisition module and is used for generating a financial intervention scheme according to the disease probability of the related diseases, the intervention measures related to the related diseases and the health record of the user.
In an embodiment of the first aspect, in the disease risk association model, each disease comprises at least one subtype, and each risk factor comprises at least one sub-term.
In an embodiment of the first aspect, the profile sub-information of the user includes a family history sub-category, a disease history sub-category, a lifestyle sub-category, a living environment sub-category and/or a mental state sub-category; the risk factors include a family history sub-item, a disease history sub-item, a lifestyle sub-item, a living environment sub-item, and/or a mental state sub-item.
In an embodiment of the first aspect, the health intervention system further includes: and the science popularization information generating module is connected with the health file acquiring module and the related disease acquiring module and is used for acquiring corresponding science popularization information according to the health file of the user and the related disease.
In an embodiment of the first aspect, the health intervention system further includes: and the risk early warning module is connected with the disease probability calculation module and used for determining the risk degree of the related diseases according to the disease probability of the related diseases and early warning a user and/or a family doctor according to the risk degree of the related diseases.
In an embodiment of the first aspect, the health intervention system further includes: the health information acquisition module is connected with the health file acquisition module and used for acquiring the health information of the user and updating the health file by utilizing the health information of the user; the health information of the user comprises symptom sub-information, index sub-information and/or archive sub-information; the disease risk correlation model also comprises symptom signs and/or examination indexes of each disease; wherein, the symptom sign of the disease is associated with the symptom sub-information of the user, and the examination index of the disease is associated with the index sub-information of the user.
In an embodiment of the first aspect, the health information obtaining module includes: the self-testing template generating unit is connected with the health file acquiring module and used for generating a self-testing template according to the health file of the user; the self-testing template is used for prompting a user to input health information; the health information receiving unit is used for receiving health information input by a user; the self-testing template updating unit is connected with the self-testing template generating unit, the health information receiving unit and the storage module and used for updating the self-testing template according to the health information of the user and the disease risk correlation model; and the updated self-testing template is used for prompting the user to continuously input the health information.
In an embodiment of the first aspect, the health intervention system further includes: and the intervention effect evaluation module is connected with the health file acquisition module and is used for evaluating the intervention effect of the intervention scheme according to the health file before the user executes the intervention scheme and the health file after the user executes the intervention scheme for a period of time.
In an embodiment of the first aspect, the intervention scheme generating module is further connected to the intervention effect evaluating module, and is configured to update the intervention scheme and/or the disease risk association model according to an evaluation result of the intervention effect.
A second aspect of the invention provides a health intervention server. The health intervention server comprises the health intervention system.
A third aspect of the invention provides a health management system. The health management system includes: the health intervention server comprises the health intervention system and is used for generating an intervention scheme according to the health record of the user and the disease risk correlation model; and the user terminal is in communication connection with the health intervention server and is used for acquiring the intervention scheme.
A fourth aspect of the invention provides a health management system. The health management system includes: the system comprises a family doctor terminal, a server and a server, wherein the family doctor terminal stores health files of at least one user; the health intervention server is in communication connection with the family doctor terminal and comprises the health intervention system; the health intervention server is used for acquiring the health file of the at least one user and generating an intervention scheme of the user according to the health file of the user; and the family doctor terminal acquires the intervention scheme of the user and provides the intervention scheme for the family doctor.
As described above, the technical solution of the health intervention system, the server and the health management system of the present invention has the following beneficial effects:
the health intervention system can acquire related diseases according to the health records of the user, and calculate the disease probability of the related diseases according to the disease risk association model. On the basis, the health intervention system generates the intervention scheme according to the prevalence probability of the related diseases and the associated intervention measures. Therefore, the health intervention system fully considers the probability that the user has the related disease or possibly has the related disease in the future, and the generated intervention scheme is high in pertinence.
Drawings
Fig. 1 is a schematic structural diagram of a health intervention system according to an embodiment of the present invention.
FIG. 2A is a diagram of an example of a disease risk association model in an embodiment of the health intervention system of the present invention.
FIG. 2B is a diagram of another example of a disease risk association model in an embodiment of the health intervention system of the present invention.
Fig. 3 is a schematic structural diagram of a related disease acquisition module in an embodiment of the health intervention system of the present invention.
Fig. 4A is a schematic structural diagram of a prevalence probability calculation module of the health intervention system according to an embodiment of the present invention.
Fig. 4B is a schematic structural diagram of a prevalence probability calculation module of the health intervention system according to another embodiment of the present invention.
FIG. 4C is a flowchart illustrating the training of the neural network model by the health intervention system of an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a health intervention system according to an embodiment of the present invention.
FIG. 6 is a diagram of an example of a disease risk association model in an embodiment of the health intervention system of the present invention.
Fig. 7 is a schematic structural diagram of a health information obtaining module in an embodiment of the health intervention system of the present invention.
Fig. 8 is a flowchart illustrating the operation of the health information obtaining module of the health intervention system according to an embodiment of the present invention.
Description of the element reference numerals
1 health intervention System
11 memory module
12 health record acquisition module
13 related disease acquisition module
131 risk factor acquiring unit
132 related disease finding unit
14 sick probability calculation module
141 weight calculation unit
142 probability calculation unit
143 training data acquisition unit
144 neural network training unit
145 neural network processing unit
15 intervention scheme generation module
16 risk early warning module
17 science popularization information generation module
18 health information acquisition module
181 self-test template generation unit
182 health information receiving unit
183 self-test template updating unit
19 intervention effect evaluation module
21 disease risk association model
22 disease risk association model
S41-S43
S81-S87
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The health intervention means that risk factors possibly causing diseases are detected and evaluated systematically to help people perform targeted preventive intervention before the diseases are formed, so that the aims of blocking, delaying or preventing the occurrence and development processes of the diseases and maintaining health are fulfilled. It can be seen that the disease is the core of healthy intervention, and since the probability that the user has or may have the disease in the future is not the same, the intervention measures adopted for different probability of the disease will also be different. However, no existing health intervention scheme takes such difference of the prevalence probability into account, so that the existing health intervention scheme is poor in pertinence. In order to solve the problem, the invention provides a health intervention system which can acquire related diseases according to health records of users and calculate the prevalence probability of the related diseases according to the disease risk correlation model. On the basis, the health intervention system generates the intervention scheme according to the prevalence probability of the related diseases and the associated intervention measures. Therefore, the health intervention system fully considers the probability that the user has the related disease or possibly has the related disease in the future, and the generated intervention scheme is high in pertinence.
Referring to fig. 1, in an embodiment of the present invention, the health intervention system 1 includes a storage module 11, a health profile management module 12, a related disease acquisition module 13, a disease probability calculation module 14, and an intervention scheme generation module 15.
The storage module 11 stores a disease risk correlation model; the disease risk association model includes a plurality of diseases, risk factors and interventions. Wherein each disease is associated with at least one risk factor and each disease is associated with at least one intervention. Such as hypertension, coronary heart disease, diabetes, etc., risk factors such as excessive alcohol consumption, smoking, low vegetable intake, etc., and interventions such as reduction of alcohol consumption, smoking cessation, increase in vegetable intake, etc.
Referring to fig. 2A, an exemplary diagram of the risk association model 21 is shown. Wherein the disease is directly associated with the risk factor and directly associated with the intervention.
Specifically, for any disease and any risk factor, if the risk factor may cause the user to suffer from the disease, an association relationship between the disease and the risk factor is considered to exist, and the association relationship is represented by a straight line between the risk factor and the disease in a graph; if the risk factor does not cause the user to suffer from the disease, the disease and the risk factor are considered to have no association. Therefore, when the user is subjected to health intervention, if the risk factor exists in the user, the user can be considered to have the disease or possibly have the disease in the future according to the association relationship. For example, for a risk factor of long-term drinking, which may cause a user to suffer from the disease of alcoholic liver, the association relationship between the disease of alcoholic liver and the risk factor of long-term drinking is considered to exist; if the health record of the user indicates that the user has the habit of drinking for a long time, the user can be considered to have or possibly have the alcoholic liver according to the correlation.
For any disease and any intervention, if the intervention blocks, delays or prevents the occurrence of the disease, an association between the intervention and the disease is considered to exist, the association being represented in the figure by a straight line between the disease and the intervention; otherwise, the intervention is considered to be not associated with the disease. Therefore, when the user is subjected to health intervention, if the user suffers from the disease or possibly suffers from the disease in the future, the user can be recommended to adopt the intervention measure according to the association relationship. For example, for the case of a diseased alcoholic liver, where the associated intervention includes a reduction in alcohol consumption, the user may be recommended to take the reduction in alcohol consumption during a health intervention to block, delay or prevent the occurrence of alcoholic liver disease.
Referring to FIG. 2B, another exemplary diagram of the risk association model 22 is shown, wherein the disease is directly associated with the risk factor and is associated with the intervention by the risk factor.
Specifically, the determination of the association relationship between the disease and the risk factors is similar to the above process, and is not repeated here. For any risk factor and any intervention measure, if the intervention measure can reduce or eliminate the risk factor so as to block, delay or prevent the occurrence of diseases, the risk factor and the intervention measure are considered to have an association relationship, and the association relationship is represented by a straight line between the risk factor and the intervention measure in a graph; if the intervention measure does not reduce or eliminate the risk factor, the risk factor is considered to be not associated with the intervention measure. When the user is subjected to health intervention, if the risk factor exists in the user, the user can be recommended to adopt the intervention measure according to the association relationship. For example, intervention to reduce alcohol consumption may mitigate the risk factor of long-term alcohol consumption, and thus a correlation between reduced alcohol consumption and long-term alcohol consumption is considered to exist; when health intervention is carried out, if the habit of long-term drinking exists in the health file of the user, the intervention measure of reducing drinking can be recommended to the user according to the association relation.
The health record obtaining module 12 is used for obtaining a health record of a user; the health profile of the user comprises profile sub-information of the user, and the profile sub-information of the user is associated with the risk factors. In this embodiment, the sub-information of the record includes basic information, health status, family history, disease history, life style, physical examination information, health care style, living environment, mental status, health general knowledge, safety awareness, and the like of the user. The basic information comprises the personal basic information of the user such as sex, age, occupation, marital status and the like. The health state comprises information such as whether the user has physical defects, whether congenital diseases exist, whether the user is short sighted and the like. The family history comprises a family medical history of the user; the disease history comprises information of previous diseases of the user; the life style comprises life information such as smoking condition, drinking condition, eating habit, exercise habit, sleeping habit and the like of the user. The physical examination information includes physical examination information of the user, for example: heart rate, liver function, blood lipid, urinary function, renal function, tumor markers, etc. The health care mode comprises information such as vaccination condition, physical examination frequency and the like. The living environment comprises information such as drinking water condition of a user, harmful substance exposure condition in work or life and the like. The mental states include life and work stress situations of the user. The health knowledge includes knowledge of the user about common sense information in terms of disease prevention, health management, and the like. The safety awareness includes the safety awareness of the user in work and life, such as whether fatigue driving is likely, whether a seat belt is worn during driving, whether a smoke sensor is installed at home, and the like.
The health file of the user can be filled by the user, and the file sub-information of the user can be acquired through corresponding health information acquisition equipment and added into the health file of the user; the health information acquisition equipment is, for example, a blood pressure meter, a weight scale, an intelligent bracelet and the like of a user.
The related disease obtaining module 13 is connected to the storage module 11 and the health record obtaining module 12, and is configured to search for a related disease from the disease risk correlation model according to the user's profile sub-information. The related diseases are diseases which are determined according to the profile sub-information of the user and are suffered by the user or possibly suffered by the user in the future. The number of the related diseases is one or more.
The disease probability calculation module 14 is connected to the related disease acquisition module 13 and the storage module 11, and is configured to calculate a disease probability of the related disease according to the disease risk association model. When the number of the related diseases is multiple, the prevalence probability calculation module 14 calculates prevalence probabilities of the related diseases, respectively.
The intervention scheme generating module 15 is connected to the prevalence probability calculating module 14 and the storage module 11, and is configured to generate an intervention scheme according to the prevalence probability of the related disease and the intervention measure associated with the related disease.
Specifically, the intervention scheme generating module 15 may select an intervention measure for the related disease according to the prevalence probability. For example, for disease 1, when the probability of developing disease is lower than a, the intervention measures associated with disease 1 include intervention measure 1 and intervention measure 2; when the prevalence probability of the disease 1 is a-b, the associated intervention measures comprise an intervention measure 1, an intervention measure 2 and an intervention measure 3; when the prevalence probability of the disease 1 is greater than b, the associated interventions include intervention 3 and intervention 4. Wherein a and b are positive numbers smaller than 1, and the value thereof can be determined according to actual requirements.
In this embodiment, the intervention scheme generation module may select a corresponding intervention measure according to the prevalence probability of the user to generate the intervention scheme, and fully considers the probability that the user suffers from or may suffer from a related disease in the future, so that the intervention scheme is more suitable for the actual situation of the user.
In an embodiment of the present invention, the number of the related diseases is plural. In the process of generating the intervention scheme, the intervention scheme generation module 15 also determines the priority of different intervention measures according to the prevalence probability of the related diseases. For example, if the related diseases of the user are disease 1 and disease 2, and the probability of suffering from disease 1 is greater than the probability of suffering from disease 2, the priority of the intervention measure associated with disease 1 is greater than the priority of the intervention measure associated with disease 2 in the intervention scheme.
Referring to fig. 3, in an embodiment of the present invention, the related disease acquiring module 13 includes a risk factor acquiring unit 131 and a related disease searching unit 132.
The risk factor acquiring unit 131 is connected to the health record acquiring module 12, and is configured to acquire a risk factor of a user according to the record sub-information of the user; wherein the risk factors associated with the profile sub-information of the user are only the risk factors of the user. For example, when the health profile of the user includes the profile sub-information "exercise frequency is less than 1 month", the risk factor of the user includes "exercise amount is low".
The related disease searching unit 132 is connected to the risk factor acquiring unit 131 and the storage module 11, and is configured to search the related disease from the disease risk correlation model according to the risk factor of the user. In the searching process, as long as a certain disease in the disease risk correlation model contains risk factors of any user, the disease is a related disease. For example, when the health profile of the user includes risk factor 1 and risk factor 2, based on the disease risk association model 22, the related disease searched by the related disease searching unit is disease 1. For another example, when the risk factors of the user include risk factor 2 and risk factor 3, the related diseases searched by the related disease searching unit are disease 1 and disease 2.
There are many ways to acquire the relevant diseases of the user according to the health file of the user, in this embodiment, the file sub-information in the health file of the user is converted into the risk factor of the user by the risk factor acquisition unit, and then the relevant diseases are searched from the disease risk association model according to the risk factor of the user, so that the acquired relevant diseases can be ensured to be more comprehensive and accurate.
Referring to fig. 4A, in an embodiment of the present invention, the prevalence probability calculating module 14 includes a weight calculating unit 141 and a probability calculating unit 142. The weight calculating unit 141 is connected to the relevant disease obtaining module 13 and the storage module 11, and is configured to calculate a weight value of a risk factor of a user according to the disease risk association model; wherein, the weight values of the risk factors of the same user in different related diseases may be different.
Specifically, for any user's risk factor m and any related disease n, the weight calculation unit 141 obtains the diagnosis criterion C of the related disease nn(ii) a Wherein, CnConsists of all risk factors associated with the relevant disease n. If the diagnostic criterion CnIf the risk factor m of the user is not included, the weight value of the risk factor m of the user in the related disease n is 0;otherwise, the weight value W of the risk factor m of the user in the related disease nm,nIs composed of
Figure BDA0002880728370000091
Wherein N ism,nIs the number of all diseases associated with the risk factor m of the user; for example, in fig. 2A, the number of all diseases associated with risk factor 3 is 2, and the number of all diseases associated with risk factor 1 is 1. N is a radical ofi,nNumber of all diseases associated with risk factor i, MnAs the diagnostic criteria CnThe number of risk factors involved, and risk factor 1, risk factor 2, … …, risk factor MnAll belong to the diagnostic criteria Cn. The above parameter Nm,n、Ni,nAnd MnCan be obtained from the disease risk correlation model.
All risk factors associated with the relevant disease n are determined by the disease risk association model. Wherein the diagnosis standard C is included in the risk factors of the patientnAll risk factors in (a), the probability that the patient suffers from the relevant disease n is considered to be 100%.
The probability calculating unit 142 is connected to the weight calculating unit 141 and the storage module 11, and is configured to calculate the probability of suffering from the related disease according to the weight of the risk factor of the user and the disease risk association model. Specifically, for the related disease n, among the risk factors of all users, the set of risk factors associated with the related disease n is Q, and then the probability P that the user has the related disease n isnComprises the following steps:
Figure BDA0002880728370000101
the sum of the weight values representing the risk factors for all users in Q. Wherein, Wj,nIs the weight value of the risk factor j of the user in the related disease n.
The above-described method for calculating the risk of disease will be described in detail below on the basis of the disease risk correlation model 21. For disease 1, criteria for diagnosis C1The method comprises the following steps: risksFactor 1, risk factor 2, and risk factor 3, and the number of all diseases N associated with risk factor 11,1Number of all diseases N associated with risk factor 2 ═ 12,1Number of all diseases associated with risk factor 3 is N ═ 13,1＝2。
If the risk factors of the user include risk factor 2, risk factor 3 and risk factor 4, the set Q of risk factors associated with the related disease n among the risk factors of all users includes risk factor 2 and risk factor 3, and:
the weight values for risk factor 2 are:
Figure BDA0002880728370000102
the weight values for risk factor 3 are:
Figure BDA0002880728370000103
based on this, the probability calculation unit 142 obtains the prevalence probability P of the disease 11＝W2,1+W3,1＝60％。
According to the embodiment, the morbidity probability of the related diseases is calculated by calculating the weight values of different risk factors, and the method does not depend on a real diagnosis case in the process, so that the workload for collecting the diagnosis case is reduced.
Referring to fig. 4B, in an embodiment of the invention, the prevalence probability calculating module 14 includes a training data obtaining unit 143, a neural network training unit 144, and a neural network processing unit 145.
The training data acquisition unit 143 is connected to the related disease acquisition module 13 and the storage module 11, and is configured to acquire training data; the training data is derived from the disease risk association model and the diagnosis case database, namely: the training data includes two types, namely training data derived from the disease risk association model and training data derived from a diagnostic case database. The diagnostic case database contains a plurality of real diagnostic cases; the real diagnosis cases include, but are not limited to, on-line inquiry cases and off-line diagnosis cases. The training data acquisition unit 143 mixes the two types of training data according to different mixing ratios, and can combine theoretical data with actual data to ensure accuracy and practicability of the probabilistic neural network model.
The training data derived from the disease risk association model refers to training data generated according to the disease risk association model. Specifically, referring to fig. 4C, for any related disease, the implementation method for generating the training data according to the disease risk association model includes:
and S41, acquiring all risk factors associated with the related diseases according to the disease risk association model. For example, based on the disease risk association model 21, all risk factors associated with disease 1 include risk factor 1, risk factor 2, and risk factor 3.
And S42, combining the risk factors acquired in the step S41 to obtain different risk factor combinations. Specifically, according to the concept of permutation and combination in mathematics, a plurality of risk factor combinations are selected from all risk factors, wherein the risk factors contained in each risk factor combination are different. For example, if all the risk factors acquired in step S41 include risk factor 1, risk factor 2, and risk factor 3, one risk factor combination acquired in step S42 may be risk factor 1, risk factor 1 and risk factor 2, risk factor 2 and risk factor 3, and so on. For the related disease n, the number of risk factor combinations that can be obtained in step S42 is at most
Figure BDA0002880728370000111
Wherein M isnThe number of all risk factors associated with the relevant disease n.
S43, calculating the disease probability corresponding to each risk factor combination; the prevalence probability can be implemented by the weight calculation unit 141 and the probability calculation unit 142, or can be implemented by other manners, which is not limited herein. And the risk factor combinations and the corresponding morbidity probabilities are the training data.
The neural network training unit 144 is connected to the training data obtaining unit 143, and is configured to train a neural network model by using the training data to obtain a trained probabilistic computational neural network model. Training the neural network model using the training data can be implemented using an existing training method, and will not be described herein again.
The neural network processing unit 145 is connected to the neural network training unit 144 and the related disease obtaining module 13, and is configured to process the risk factors of the user by using the probability calculation neural network model to obtain the prevalence probability of the related disease. Specifically, the neural network processing unit 145 uses the risk factor of the user as an input of the probability calculation neural network model, and an output of the probability calculation neural network model is the prevalence probability of the related disease.
The training data obtaining unit may obtain the training data only according to risk factors and diagnosis cases associated with one related disease, and the neural network processing unit may obtain the prevalence probability of the one related disease by using the probability calculation neural network model.
The training data acquisition unit can also acquire the training data according to risk factors and diagnosis cases related to various related diseases, and the neural network processing unit can simultaneously acquire the disease probability of the various related diseases by utilizing the probability calculation neural network model.
In an embodiment of the present invention, the intervention scheme generating module includes a habit intervention unit, a medicine intervention unit, a doctor intervention unit, a knowledge intervention unit, and/or a financial intervention unit.
The habit intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a life habit intervention scheme according to the morbidity probability of the related diseases, intervention measures related to the related diseases and the health record of the user. The life habit intervention scheme is an intervention scheme generated by selecting intervention measures which are related to the life habits of the user and are related to the related diseases from the disease risk correlation model. The lifestyle intervention programs are for example: properly reducing the intake of livestock meat, continuously keeping low-fat food, increasing the intake of vegetables and light diet. The lifestyle intervention programs are applicable regardless of the prevalence probability of the associated disease.
The drug intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a drug intervention scheme according to the morbidity probability of the related diseases, intervention measures related to the related diseases and the health record of the user. The medicine intervention scheme comprises medicine recommendation, medicine guidance and the like and is used for the condition that the disease probability is larger than a first threshold value. The first threshold is an empirical value, and the value of the first threshold can be set according to actual requirements.
The hospitalizing intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a hospitalizing intervention scheme according to the morbidity probability of the related diseases, the intervention measures related to the related diseases and the health record of the user. Wherein the hospitalization intervention plan includes recommendations for hospitalization departments, conditions, hospitals, etc., for cases where the prevalence probability is greater than a second threshold. The second threshold is also an empirical value, and the value of the second threshold can be set according to actual requirements.
The knowledge intervention unit is connected with the disease probability calculation module, the storage module and the health record acquisition module and is used for generating a knowledge intervention scheme according to the disease probability of the related diseases, the intervention measures related to the related diseases and the health record of the user. Wherein the knowledge intervention scheme comprises one or more knowledge intervention measures for providing the user with knowledge and/or science popularization information of the relevant diseases. For example, if the relevant illness of the user includes hypertension, the knowledge intervention measure includes hypertension-related knowledge and/or science popularization information. The knowledge intervention measure may be selected from the disease risk association model. The knowledge intervention protocol is applicable regardless of the prevalence probability of the associated disease.
The financial intervention unit is connected with the disease probability calculation module, the storage module and the health record acquisition module and is used for generating a financial intervention scheme according to the disease probability of the related diseases, the intervention measures related to the related diseases and the health record of the user. Wherein the financial intervention program comprises one or more financial intervention measures for providing the user with a financial program related to the related disease or the user's health condition, including but not limited to financial purchases, insurance purchases, and the like. The financial intervention may be selected from the disease risk correlation model. The financial intervention scheme is applicable regardless of the prevalence probability of the associated disease.
In an embodiment of the present invention, in the disease risk association model, each disease includes at least one subtype, and each risk factor includes at least one sub-item. For example, for the risk factor of drinking, the disease risk association model includes sub-items of daily drinking, weekly drinking, monthly drinking, and the like. The subtype refers to a combination of symptom signs, examination index, familial inheritance, disease history, medication history, age and/or gender, etc., which can be used to determine the disease type, for example: fever greater than 40 ℃ plus white blood cell count greater than 1000 can be considered one subtype, fever greater than 37 ℃ plus white blood cell count greater than 500 can be considered another subtype.
In this embodiment, the disease risk association model further subdivides the disease and the risk factors, so that the content of the disease risk association model is richer, and the accuracy of the intervention scheme is improved.
In an embodiment of the present invention, the profile sub-information of the user includes a family history sub-category, a disease history sub-category, a lifestyle sub-category, a living environment sub-category and/or a psychological state sub-category; the risk factors include a family history sub-item, a disease history sub-item, a lifestyle sub-item, a living environment sub-item, and/or a mental state sub-item. Wherein the family history sub-item of the risk factor is associated with the family history sub-category of the archive sub-information, the disease history sub-item of the risk factor is associated with the disease history sub-category of the archive sub-information, the lifestyle sub-item of the risk factor is associated with the lifestyle sub-category of the archive sub-information, the living environment sub-item of the risk factor is associated with the living environment sub-category of the archive sub-information, and the mental state sub-item of the risk factor is associated with the mental state sub-category of the archive sub-information.
In one embodiment of the invention, the intervention regimen comprises a prophylactic regimen, a therapeutic regimen and/or a rehabilitation regimen. Wherein the prevention scheme is an intervention scheme for a disease that the user may have in the future, the treatment scheme is an intervention scheme for a disease that the user has already suffered from but has not yet begun to treat, and the rehabilitation scheme is an intervention scheme for a disease that the user has begun to treat but has not yet cured.
Referring to fig. 5, in an embodiment of the present invention, the health intervention system 1 further includes: a science popularization information generating module 17, connected to the health record acquiring module 12 and the related disease acquiring module 13, for acquiring corresponding science popularization information according to the health record of the user and the related disease. The science popularization information can be pushed to the user or a family doctor of the user, and the family doctor transmits the science popularization information to the user.
Most of the existing medical science popularization schemes determine the content of pushed science popularization information based on the browsing habits of users, and the users do not necessarily pay attention to the information related to the health conditions of the users during browsing. The content of the pushed science popularization information is determined directly based on the health profile of the user and related diseases suffered by the user or possibly suffered by the user in the future. Therefore, the pushing of the science popularization information in the embodiment is closer to the health condition of the user, the health awareness of the user is favorably improved, and the enthusiasm of the user for executing the intervention scheme is further improved.
In an embodiment of the present invention, the health intervention system 1 further includes a risk pre-warning module 16. The risk early warning module 16 is connected to the disease probability calculation module 14, and configured to determine a risk degree of the related disease according to the disease probability of the related disease, and perform early warning on a user and/or a family doctor according to the risk degree of the related disease. Specifically, the risk early warning module 16 may determine the risk degree of the suspected disease according to the prevalence probability, the hazard degree, and the infection degree of the suspected disease in the self-test report, and when the user is in a high risk state or a high infection state, perform early warning to the user and/or a family doctor in a manner of voice, text information, and the like.
In an embodiment of the present invention, the health intervention system 1 further includes a health information obtaining module 18. The health information acquiring module 18 is connected to the health file acquiring module 12, and is configured to acquire health information of a user and update the health file with the health information of the user; wherein the health information of the user comprises symptom sub-information, index sub-information and/or archive sub-information.
In particular, the symptom sub-information includes relevant symptom signs physically or psychologically exhibited by the user, such as: fever, dry cough, hypodynamia, dyspnea and the like, and the user can determine the symptom sub-information through the physical expression of the user. The indicator sub-information includes physical indicators describing the physical condition of the user in a quantitative manner, such as: blood pressure, body temperature, white blood cell count, hemoglobin, etc.; the user can obtain the index sub-information through corresponding medical equipment, and can also obtain the index sub-information through modes such as hospital physical examination and the like.
Referring to fig. 6, in the present embodiment, the disease risk correlation model further includes symptom signs and/or examination indexes of each disease; wherein, the symptom sign of the disease is associated with the symptom sub-information of the user, and the examination index of the disease is associated with the index sub-information of the user.
In this embodiment, the health information obtaining module 18 updates the health profile of the user by using the symptom sub-information, the index sub-information and/or the profile sub-information, so that the information in the health profile is more complete and the timeliness is stronger. Updating the health profile of the user includes adding the health information to the health profile of the user or replacing original information in the health profile of the user with the health information. In addition, the related disease obtaining module 13 may also search the related disease in the disease risk correlation model according to the symptom sub-information, the index sub-information and/or the archive sub-information, which is beneficial to improving the accuracy of searching the related disease.
Referring to fig. 7, in an embodiment of the present invention, the health information obtaining module 18 includes a self-test template generating unit 181, a health information receiving unit 182, and a self-test template updating unit 183.
The self-test template generating unit 181 is connected to the health archive acquiring module 12, and is configured to generate a self-test template according to the health archive of the user; the self-test template generated by the self-test template generating unit 181 is an initial self-test template for prompting the user to input health information. Preferably, the self-test template generating unit 181 generates the initial self-test template according to the health profile of the user, the current season, the prevalence of diseases, and the like.
The health information receiving unit 182 is used for receiving health information input by a user.
The self-test template updating unit 183 is connected to the self-test template generating unit 181, the health information receiving unit 182, and the storage module, and is configured to update the self-test template according to the health information of the user and the disease risk association model; and the updated self-testing template is used for prompting the user to continuously input the health information.
Specifically, referring to fig. 8, the workflow of the health information obtaining module 18 obtaining the health information of the user includes:
s81, the self-test template generating unit 181 generates a self-test template, and the user inputs the health information according to the self-test template; wherein the health information comprises symptom sub-information, index sub-information and/or archive sub-information.
S82, the health information receiving unit 182 receives the health information input by the user.
S83, the self-test template updating unit 183 obtains the health information input by the user from the health information receiving unit 182, and obtains the related disease information according to an association relationship. Wherein, the related disease information refers to all disease information related to the health information input by the user, which includes symptom signs, examination indexes and/or risk factors. The association relationship comprises: the symptom signs are associated with symptom sub-information, the examination index is associated with index sub-information, and the risk factor is associated with archive sub-information. For example, if the health information input by the user is body temperature 39 ℃, the disease information related to the health information is fever symptoms, and the body temperature 39 ℃ is an examination index.
S84, the self-test template updating unit 183 searches the disease risk association model for possible diseases according to the related disease information. The possible disease is a disease associated with all or part of the disease information included in the related disease information.
S85, the self-test template updating unit 183 obtains all the disease information of the possible diseases according to the disease risk correlation model, and obtains the health information related to the possible diseases according to all the disease information of the possible diseases.
S86, updating the current self-testing template to obtain an updated self-testing template; the updated self-test template includes the health information acquired in step S85, and prompts the user to select health information corresponding to the self-test template for input.
S87, the steps S82 to S86 are repeated until the health information obtaining module 18 obtains enough health information.
In an embodiment of the present invention, the health intervention system 1 further includes an intervention effect evaluation module 19. The intervention effect evaluation module 19 is connected to the health profile obtaining module 12, and is configured to evaluate an intervention effect of the intervention program according to the first health profile and the second health profile. Wherein the first health profile refers to a health profile of the user before the intervention program is executed, and the second health profile refers to a health profile of the user after the intervention program is executed for a period of time.
Specifically, the intervention effect evaluation module 19 compares the first health profile with the second health profile to obtain symptom sub-information, index sub-information and/or profile sub-information of the user having an improvement after the intervention program is executed for a period of time, and obtains symptom sub-information, index sub-information and/or profile sub-information of the user having an deterioration after the intervention program is executed for a period of time.
In an embodiment of the present invention, the intervention scheme generating module 15 is further connected to the intervention effect evaluating module 19, and is configured to update the intervention scheme and/or the disease risk correlation model according to the evaluation result of the intervention effect. Specifically, in the intervention scheme, intervention measures capable of improving the symptom sub-information, the index sub-information and/or the archive sub-information of the user are reserved or enhanced, and intervention measures causing deterioration of the symptom sub-information, the index sub-information and/or the archive sub-information of the user are weakened or eliminated, so that a good intervention effect of the updated intervention scheme can be ensured. In addition, in the intervention scheme, an intervention measure which is ineffective or can cause the related disease to be worsened does not have an association relation with the related disease; the intervention scheme generation module 15 updates the association relationship in the disease risk association model based on this.
According to the above description of the health intervention system, the invention also provides a health intervention server. The health intervention server comprises the health intervention system.
According to the above description of the health intervention system, the invention also provides a health management system. The health management system comprises a health intervention server and a user terminal. The health intervention server comprises the health intervention system and is used for generating an intervention scheme according to the health record of the user and the disease risk correlation model; and the user terminal is in communication connection with the health intervention server and is used for acquiring the intervention scheme.
According to the above description of the health intervention system, the present invention also provides another health management system. The health management system comprises a family doctor terminal and a health intervention server. The family doctor terminal stores health files of at least one user; the health intervention server is in communication connection with the family doctor terminal and comprises the health intervention system; the health intervention server is used for acquiring the health record of the at least one user and generating an intervention scheme of the user according to the health record of the user. The family doctor terminal acquires the user intervention scheme when needed and provides the user intervention scheme for a family doctor.
The health intervention system can acquire related diseases according to the health records of the user, and calculate the disease probability of the related diseases according to the disease risk association model. On the basis, the health intervention system generates the intervention scheme according to the prevalence probability of the related diseases and the associated intervention measures. Therefore, the health intervention system fully considers the probability that the user has the related disease or possibly has the related disease in the future, and the generated intervention scheme is high in pertinence.
In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (16)

1. A health intervention system, characterized in that the health intervention system comprises:
the storage module stores a disease risk correlation model; the disease risk association model comprises a plurality of diseases, risk factors and intervention measures; wherein each disease is associated with at least one risk factor and at least one intervention;
the health file acquisition module is used for acquiring a health file of a user; the health profile of the user comprises profile sub-information of the user, and the profile sub-information of the user is associated with the risk factors;
the related disease acquisition module is connected with the storage module and the health file acquisition module and used for searching related diseases from the disease risk correlation model according to the health file of the user;
the illness probability calculation module is connected with the related disease acquisition module and the storage module and used for calculating the illness probability of the related disease according to the disease risk correlation model;
and the intervention scheme generation module is connected with the morbidity probability calculation module and the storage module and is used for generating an intervention scheme according to the morbidity probability of the related diseases and the intervention measures related to the related diseases.
2. The health intervention system of claim 1, wherein the relevant disease acquisition module comprises:
the risk factor acquisition unit is connected with the health file acquisition module and used for acquiring the risk factors of the user according to the file sub-information of the user;
and the related disease searching unit is connected with the risk factor acquiring unit and the storage module and is used for searching the related diseases from the disease risk correlation model according to the risk factors of the user.
3. The health intervention system of claim 1, wherein the prevalence probability calculation module comprises:
the weight calculation unit is connected with the related disease acquisition module and the storage module and is used for calculating the weight value of the risk factor of the user according to the disease risk association model;
and the probability calculation unit is connected with the weight calculation unit and the storage module and is used for calculating the disease probability of the related diseases according to the weight value of the risk factor of the user and the disease risk association model.
4. The health intervention system of claim 1, wherein the prevalence probability calculation module comprises:
the training data acquisition unit is connected with the storage module and used for acquiring training data; the training data is derived from the disease risk association model and a diagnostic case database;
the neural network training unit is connected with the training data acquisition unit and used for training a neural network model by using the training data to obtain a trained probability calculation neural network model;
and the neural network processing unit is connected with the neural network training unit and the related disease acquisition module and is used for processing the risk factors of the user by utilizing the probability calculation neural network model so as to obtain the disease probability of the related disease.
5. The health intervention system of claim 1, wherein the intervention scenario generation module comprises a habit intervention unit, a drug intervention unit, a hospitalization intervention unit, a knowledge intervention unit, and/or a financial intervention unit;
the habit intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a life habit intervention scheme according to the morbidity probability of the related diseases, intervention measures related to the related diseases and the health record of the user;
the drug intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a drug intervention scheme according to the morbidity probability of the related diseases, intervention measures related to the related diseases and the health record of the user;
the hospitalizing intervention unit is connected with the morbidity probability calculation module, the storage module and the health record acquisition module and is used for generating a hospitalizing intervention scheme according to the morbidity probability of the related diseases, the intervention measures related to the related diseases and the health record of the user;
the knowledge intervention unit is connected with the disease probability calculation module, the storage module and the health record acquisition module and is used for generating a knowledge intervention scheme according to the disease probability of the related diseases, the intervention measures related to the related diseases and the health record of the user;
the financial intervention unit is connected with the disease probability calculation module, the storage module and the health record acquisition module and is used for generating a financial intervention scheme according to the disease probability of the related diseases, the intervention measures related to the related diseases and the health record of the user.
6. The health intervention system of claim 1, wherein: in the disease risk association model, each disease comprises at least one subtype, and each risk factor comprises at least one sub-item.
7. The health intervention system of claim 1, wherein:
the user's profile sub-information includes a family history sub-category, a disease history sub-category, a lifestyle sub-category, a living environment sub-category and/or a psychological state sub-category;
the risk factors include a family history sub-item, a disease history sub-item, a lifestyle sub-item, a living environment sub-item, and/or a mental state sub-item.
8. The health intervention system of claim 1, further comprising:
and the science popularization information generating module is connected with the health file acquiring module and the related disease acquiring module and is used for acquiring corresponding science popularization information according to the health file of the user and the related disease.
9. The health intervention system of claim 1, further comprising:
and the risk early warning module is connected with the disease probability calculation module and used for determining the risk degree of the related diseases according to the disease probability of the related diseases and early warning a user and/or a family doctor according to the risk degree of the related diseases.
10. The health intervention system of claim 1, further comprising:
the health information acquisition module is connected with the health file acquisition module and used for acquiring the health information of the user and updating the health file by utilizing the health information of the user; the health information of the user comprises symptom sub-information, index sub-information and/or archive sub-information;
the disease risk correlation model also comprises symptom signs and/or examination indexes of each disease; wherein, the symptom sign of the disease is associated with the symptom sub-information of the user, and the examination index of the disease is associated with the index sub-information of the user.
11. The health intervention system of claim 10, wherein the health information acquisition module comprises:
the self-testing template generating unit is connected with the health file acquiring module and used for generating a self-testing template according to the health file of the user; the self-testing template is used for prompting a user to input health information;
the health information receiving unit is used for receiving health information input by a user;
the self-testing template updating unit is connected with the self-testing template generating unit, the health information receiving unit and the storage module and used for updating the self-testing template according to the health information of the user and the disease risk correlation model; and the updated self-testing template is used for prompting the user to continuously input the health information.
12. The health intervention system of claim 1, further comprising:
and the intervention effect evaluation module is connected with the health file acquisition module and is used for evaluating the intervention effect of the intervention scheme according to the health file before the user executes the intervention scheme and the health file after the user executes the intervention scheme for a period of time.
13. The health intervention system of claim 12, wherein: the intervention scheme generation module is also connected with the intervention effect evaluation module and is used for updating the intervention scheme and/or the disease risk correlation model according to the evaluation result of the intervention effect.
14. A health intervention server, characterized by: the health intervention server comprising the health intervention system of any of claims 1 to 13.
15. A health management system, characterized in that the health management system comprises:
a health intervention server comprising the health intervention system of any one of claims 1 to 13, for generating an intervention program based on the health profile of the user and the disease risk correlation model;
and the user terminal is in communication connection with the health intervention server and is used for acquiring the intervention scheme.
16. A health management system, characterized in that the health management system comprises:
the system comprises a family doctor terminal, a server and a server, wherein the family doctor terminal stores health files of at least one user;
a health intervention server, communicatively connected to the family doctor terminal, comprising the health intervention system of any one of claims 1 to 13; the health intervention server is used for acquiring the health file of the at least one user and generating an intervention scheme of the user according to the health file of the user;
and the family doctor terminal acquires the intervention scheme of the user and provides the intervention scheme for the family doctor.
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