Heart and cerebral vessel health management method and system based on deep learning and genetic environment interaction algorithm

Abstract

The invention discloses a heart and cerebral vessel health management method and system based on a deep learning and genetic environment interaction algorithm. The method comprises the following steps: acquiring genetic gene data, physiological index data, environmental data and living habit data; constructing a cardiovascular and cerebrovascular disease risk prediction model based on a deep learning algorithm, and processing the obtained data by using the cardiovascular and cerebrovascular disease risk prediction model to obtain a cardiovascular and cerebrovascular disease onset risk prediction result; constructing a genetic environment interaction analysis model by using a genetic environment interaction algorithm, and analyzing the interaction between the genetic gene data and the environmental data by using the genetic environment interaction analysis model to obtain an assessment result of the genetic gene data on the cardiovascular and cerebrovascular disease risk; and according to the prediction result and the evaluation result, generating a user personalized health management scheme. According to the scheme, genetic genes, living behaviors, environmental factors and physiological indexes of individuals can be comprehensively analyzed, and making of a personalized health management scheme for cardiovascular and cerebrovascular diseases is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and particularly to a cardiovascular and cerebrovascular health management method and system based on a deep learning and genetic environment interaction algorithm. Background Art

[0002] Cardiovascular and cerebrovascular diseases are one of the main causes of death and disability globally. With the aggravation of population aging and the change of lifestyle, the incidence of cardiovascular and cerebrovascular diseases shows an upward trend. The number of people who die suddenly from cardiac causes in China is about 550,000 per year, and at least 1,500 people experience cardiac arrest every day, ranking first in the world. However, less than 1% of them are successfully rescued. Therefore, ultra-early warning is particularly important; cardiovascular and cerebrovascular diseases are complex diseases, and sudden cardiac death is very difficult to evaluate. 50% of the cases have no clinical manifestations, and there are asymptomatic or mildly symptomatic cases, and the first symptom is sudden death. Conventional detection methods, such as auscultation, electrocardiogram, cardiovascular angiography, CT examination, etc., can play a certain helping role, but for some occult diseases related to sudden death, such as QT syndrome, coronary heart disease, and diseases with a coronary artery stenosis degree less than 50%, they are completely ineffective. Moreover, about 70% of sudden cardiac deaths are not suitable for defibrillator treatment.

[0003] Early diagnosis and treatment are crucial for preventing cardiovascular events. Cardiovascular and cerebrovascular diseases are complex diseases, affected by genetic factors (multiple genes) and traditional risk factors (hypertension, dyslipidemia, etc.) together. Evaluating the sudden death risk at the gene level is crucial for preventing sudden death. By comprehensively evaluating the genetic risk (Genetic Risk) of cardiovascular and cerebrovascular diseases from the detection of multiple genes and single genes, and predicting the risk probability, the occurrence probability of sudden death can be effectively reduced by implementing preventive solutions. The comprehensive evaluation model of multiple gene genetic risk + traditional risk has a more accurate risk prediction ability, so as to achieve individualized and precise prevention and control.

[0004] At present, the prevention and management of cardiovascular and cerebrovascular diseases mainly rely on traditional physical examination indicators and doctors' experience judgment, lacking comprehensive analysis of individual genetic genes, life behaviors, and environmental factors, and it is difficult to achieve precise health management and early warning. Summary of the Invention

[0005] To solve the technical problem that the existing prevention and management of cardiovascular and cerebrovascular diseases mainly rely on traditional physical examination indicators and doctors' experience judgment, lacking comprehensive analysis of individual genetic genes, life behaviors, environmental factors, etc., the embodiments of the present invention provide a cardiovascular and cerebrovascular health management method and system based on a deep learning and genetic environment interaction algorithm.

[0006] The technical solutions of the embodiments of the present invention are realized as follows:

[0007] An embodiment of the present invention provides a cardiovascular and cerebrovascular health management method based on a deep learning and genetic-environment interaction algorithm. The method includes: obtaining genetic gene data, physiological index data, environmental data, and lifestyle data of a user; constructing a cardiovascular and cerebrovascular disease risk prediction model based on a deep learning algorithm, and using the cardiovascular and cerebrovascular disease risk prediction model to process the genetic gene data, the physiological index data, the environmental data, and the lifestyle data to obtain a prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model; constructing a genetic-environment interaction analysis model using a genetic-environment interaction algorithm, and using the genetic-environment interaction analysis model to analyze the interaction between the genetic gene data and the environmental data to obtain an evaluation result of the genetic gene data on the risk of cardiovascular and cerebrovascular diseases; generating a personalized health management plan for the user according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases and the evaluation result of the genetic gene data on the risk of cardiovascular and cerebrovascular diseases; the personalized health management plan for the user includes lifestyle intervention suggestions.

[0008] In one embodiment, obtaining genetic gene data, physiological index data, environmental data, and lifestyle data of a user includes: obtaining genetic gene data, physiological index data, environmental data, and lifestyle data of the user by means of uploading through a wearable device and / or self-filling and uploading by the user.

[0009] In one embodiment, after obtaining genetic gene data, physiological index data, environmental data, and lifestyle data of the user, the method further includes: performing cleaning, standardization, and feature extraction operations on the genetic gene data, the physiological index data, the environmental data, and the lifestyle data.

[0010] In one embodiment, using the cardiovascular and cerebrovascular disease risk prediction model to process the genetic gene data, the physiological index data, the environmental data, and the lifestyle data includes: determining weights corresponding to the genetic gene data, the physiological index data, the environmental data, and the lifestyle data respectively; obtaining scoring results output by the cardiovascular and cerebrovascular disease risk prediction model for the genetic gene data, the physiological index data, the environmental data, and the lifestyle data respectively; and calculating based on the scoring results and the weight values to obtain a prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model.

[0011] In one embodiment, calculating based on the scoring results and the weight values to obtain a prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model includes: calculating based on the scoring results and the weight values using the following calculation formula:

[0012] Risk Score = f(Risk Score')

[0013] Risk Score' = wg * G + wp * P + we * E + wl * L

[0014] Among them, Risk Score is the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, f() is a non - linear function, including logarithmic, exponential or piece - wise functions; Risk Score' is the initial prediction result of the onset risk of cardiovascular and cerebrovascular diseases, wg is the weight value of genetic gene data; wp is the weight value of physiological index data; we is the weight value of environmental data; wl is the weight value of lifestyle data; G is genetic gene data; P is physiological index data; E is environmental data; L is lifestyle data.

[0015] In one embodiment, after obtaining the prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model, the method further includes: determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases.

[0016] In one embodiment, determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases includes:

[0017] Determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases by using the following calculation formula:

[0018]

[0019] Among them, Risk Score is the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, and T1 and T2 are judgment thresholds.

[0020] In one embodiment, after determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, the method further includes: when it is determined that the risk level is high - risk, sending out a warning signal.

[0021] The embodiment of the present invention also provides a cardiovascular and cerebrovascular health management system based on a deep learning and genetic - environment interaction algorithm, including: a processor and a memory for storing a computer program that can run on the processor; wherein, when the processor is used to run the computer program, it executes the steps of the above - mentioned method.

[0022] The embodiment of the present invention also provides a storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it realizes the steps of the above - mentioned method.

[0023] The embodiment of the present invention has the following beneficial effects:

[0024] In the embodiments of the present invention, by comprehensively analyzing an individual's genetic information, lifestyle, environmental factors, and physiological indicators, a risk prediction model for cardiovascular and cerebrovascular diseases is constructed using deep learning algorithms, and combined with genetic-environment interaction analysis, accurate prediction of cardiovascular and cerebrovascular diseases and formulation of personalized health management plans can be achieved. Compared with the prior art, this embodiment has higher prediction accuracy and personalization degree, which helps to reduce the incidence and mortality of cardiovascular and cerebrovascular diseases and improve the quality of life of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic flowchart of a cardiovascular and cerebrovascular health management method based on deep learning and genetic-environment interaction algorithms in the embodiments of the present invention;

[0026] Figure 2 It is a schematic application flowchart in the embodiments of the present invention;

[0027] Figure 3 It is an internal structure diagram of a computer device in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The present invention will be further described in detail below in conjunction with the drawings and embodiments.

[0029] The embodiments of the present invention provide a cardiovascular and cerebrovascular health management method based on deep learning and genetic-environment interaction algorithms, as Figure 1 shown, the method includes:

[0030] Step 101: Obtain the user's genetic data, physiological index data, environmental data, and living habit data;

[0031] Step 102: Construct a risk prediction model for cardiovascular and cerebrovascular diseases based on deep learning algorithms, and use the risk prediction model for cardiovascular and cerebrovascular diseases to process the genetic data, the physiological index data, the environmental data, and the living habit data to obtain the risk prediction result of the onset of cardiovascular and cerebrovascular diseases output by the risk prediction model for cardiovascular and cerebrovascular diseases;

[0032] Step 103: Construct a genetic-environment interaction analysis model using genetic-environment interaction algorithms, and use the genetic-environment interaction analysis model to analyze the interaction between the genetic data and the environmental data to obtain an evaluation result of the genetic data on the risk of cardiovascular and cerebrovascular diseases;

[0033] Step 104: Generate a personalized health management plan for the user according to the risk prediction result of the onset of cardiovascular and cerebrovascular diseases and the evaluation result of the genetic data on the risk of cardiovascular and cerebrovascular diseases; the personalized health management plan for the user includes lifestyle intervention suggestions.

[0034] This embodiment can comprehensively analyze an individual's genetic genes, lifestyle behaviors, environmental factors, and physiological indicators to achieve accurate prediction of cardiovascular and cerebrovascular diseases and formulation of personalized health management plans.

[0035] Specifically, this embodiment can obtain the user's genetic gene data, physiological index data, environmental data, and lifestyle habit data through the way of uploading by wearable devices and / or self-filling and uploading by the user.

[0036] Here, the genetic gene data related to cardiovascular and cerebrovascular diseases include:

[0037] (1) The first category of polygenic genetic risk:

[0038] Table 1

[0039]

[0040]

[0041]

[0042] (2) Monogenic genetic cardiovascular diseases:

[0043] Table 2

[0044]

[0045]

[0046] (3) Genes related to the metabolism of cardiovascular disease treatment drugs:

[0047] Table 3

[0048]

[0049] Other health data collected include: heart rate, heart rhythm, cardiac electrophysiological data, body temperature, blood pressure, age, gender, eating habits, lifestyle habits (sedentary, staying up late, lack of sleep)

[0050] Table 4

[0051] Age Gender Low-density lipoprotein cholesterol High-density lipoprotein cholesterol Total cholesterol Body temperature Systolic blood pressure Diastolic blood pressure Triglyceride Lipoprotein Heart rate Blood pressure Smoking Alcohol consumption Staying up late Sleep duration

[0052] After this embodiment obtains the user's genetic gene data, physiological index data, environmental data, and lifestyle habit data, it is also necessary to perform cleaning, standardization, and feature extraction operations on the genetic gene data, the physiological index data, the environmental data, and the lifestyle habit data.

[0053] Specifically, the following processing is carried out:

[0054] Data preprocessing includes:

[0055] Genetic data: Extract gene loci (SNPs) related to diseases or health risks and perform weighting processing.

[0056] Physiological indicators: Such as blood pressure, blood sugar, cholesterol, etc., standardized to a unified range.

[0057] Environmental factors: Such as air quality, living environment, etc., quantified into computable indicators.

[0058] Living habits: Such as diet, exercise, smoking, alcohol consumption, etc., converted into numerical scores.

[0059] After completing the preprocessing, this embodiment performs the following calculations on the preprocessed data:

[0060] Determine the weights corresponding to the genetic gene data, the physiological indicator data, the environmental data, and the living habit data respectively; obtain the scoring results output by the cardiovascular disease risk prediction model for the genetic gene data, the physiological indicator data, the environmental data, and the living habit data respectively; perform calculations based on the scoring results and the weight values to obtain the cardiovascular disease incidence risk prediction result output by the cardiovascular disease risk prediction model.

[0061] Since the data in each dimension has different impacts on the risk, it is necessary to assign weights to each feature. The weights can be determined by machine learning models (such as linear regression, random forest, etc.) or expert experience.

[0062] Here, let the weight of the gene feature be wg

[0063] The weight of the physiological indicator be wp

[0064] The weight of the environmental factor be we

[0065] The weight of the living habit be wl

[0066] Multiply the score of each dimension by its weight and sum to obtain the comprehensive risk score:

[0067] Risk Score = f(Risk Score')

[0068] Risk Score' = wg * G + wp * P + we * E + wl * L

[0069] Among them, Risk Score is the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, f() is a non-linear function, including logarithmic, exponential or piecewise functions; Risk Score' is the initial prediction result of the onset risk of cardiovascular and cerebrovascular diseases, wg is the weight value of genetic gene data; wp is the weight value of physiological index data; we is the weight value of environmental data; wl is the weight value of lifestyle data; G is genetic gene data; P is physiological index data; E is environmental data; L is lifestyle data.

[0070] Since there may be non-linear relationships for some factors (such as age and disease risk), therefore, in this embodiment, adjustment is made by introducing a non-linear function (such as logarithmic, exponential or piecewise functions).

[0071] For the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, the corresponding risk level can also be determined according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases.

[0072] Specifically, the corresponding risk level is determined according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases by using the following calculation formula:

[0073]

[0074] Among them, Risk Score is the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, and T1 and T2 are judgment thresholds, which can be determined by statistical methods or clinical experience.

[0075] In this embodiment, a warning signal can be issued when the judged risk level is high risk.

[0076] In this embodiment, as new data is added (such as new physiological indexes or lifestyle changes), the weights and scores can be dynamically updated.

[0077] In this embodiment, by comprehensively analyzing the genetic information, lifestyle, environmental factors and physiological indexes of an individual, a risk prediction model for cardiovascular and cerebrovascular diseases is constructed by using a deep learning algorithm, and combined with genetic-environment interaction analysis, accurate prediction of cardiovascular and cerebrovascular diseases and formulation of personalized health management plans can be realized. Compared with the prior art, this embodiment has higher prediction accuracy and personalization degree, helps to reduce the incidence and mortality of cardiovascular and cerebrovascular diseases, and improves the quality of life of patients.

[0078] Next, a specific embodiment will be used for illustration.

[0079] See Figure 2 , the method of this embodiment includes the following steps:

[0080] Data collection: Passively collect life indicators, physical conditions, environmental data through wearable devices, and actively upload data on smoking, drinking, and medication by users. Collect genetic data through WES / WGS and upload it to the system.

[0081] Data preprocessing: Clean, standardize, and extract features from the collected data, remove noise data and missing values, and extract features related to cardiovascular and cerebrovascular diseases.

[0082] Deep learning model training: Use DS to perform deep training on the preprocessed data to learn the model (the name of the model DS is open source and free), optimize the model parameters, and improve the prediction accuracy of the model.

[0083] Genetic-environment interaction analysis for specific users: Use the established large model above to analyze the data between users' genetic factors and environmental factors, evaluate their impact on the risk of cardiovascular and cerebrovascular diseases, and form an evaluation result of disease and non-disease states.

[0084] Generation of health management plan: Generate a personalized health management plan according to the risk prediction results and genetic-environment interaction analysis results.

[0085] Real-time monitoring, warning, and correction of health management plan: Real-time monitor the health data of individuals. When it is detected that the risk of cardiovascular and cerebrovascular diseases increases significantly, send out warning signals in a timely manner and correct the health management plan.

[0086] Genetic counseling and treatment guidance: Users can initiate genetic counseling and health counseling on the system. The system will automatically extract the relevant health data of users, and combine with the problem description of users, and use natural language processing technology and intelligent diagnosis algorithms to generate preliminary answers and suggestions. The system will search the built-in knowledge base, which contains rich medical knowledge, treatment plans for common diseases, drug information, etc. At the same time, the system will also refer to the personalized health model and historical consultation records of users to provide more targeted treatment guidance for users.

[0087] To implement the method of the embodiments of the present invention, the embodiments of the present invention also provide a cardiovascular and cerebrovascular health management system based on deep learning and genetic-environment interaction algorithm, including: a processor and a memory for storing a computer program that can run on the processor; wherein, when the processor is used to run the computer program, it executes the steps of the above-mentioned method.

[0088] Specifically, in the actual application process, the system of this embodiment may include the following modules:

[0089] Data collection module: Collect genetic data, physiological index data, lifestyle data, and environmental data of individuals through wearable devices and data filled in and uploaded by users themselves.

[0090] Data preprocessing module: Clean, standardize, and extract features from the collected data.

[0091] Deep learning model construction module: Construct a risk prediction model for cardiovascular and cerebrovascular diseases based on deep learning algorithms. The model inputs include genetic genes, physiological indicators, lifestyle, and environmental factors, and the output is the incidence risk of cardiovascular and cerebrovascular diseases.

[0092] Genetic-environment interaction analysis module: Analyze the interaction between genetic factors and environmental factors and evaluate its impact on the risk of cardiovascular and cerebrovascular diseases.

[0093] Health management plan generation module: Generate personalized health management plans based on the risk prediction results and genetic-environment interaction analysis results, including lifestyle intervention suggestions, drug treatment suggestions, etc.

[0094] Early warning module: Real-time monitor the health data of individuals through wearable devices and send out early warning signals in a timely manner when a significant increase in the risk of cardiovascular and cerebrovascular diseases is detected.

[0095] Core functions

[0096] Personalized health assessment: Obtain the user's genetic data and real-time health data through data such as wearable devices and test results, and generate a personalized health report. For example, assess the user's risk of diseases such as heart disease and diabetes.

[0097] Disease risk prediction: Predict the possible future health problems of users by analyzing genetic data, physiological data, environmental factors, and lifestyle behaviors.

[0098] Health advice and intervention: Provide targeted health advice (such as diet, exercise, drugs, etc.) based on the analysis results. For example, if the user carries a certain gene mutation, the system may recommend avoiding certain drugs or foods.

[0099] Real-time monitoring and early warning: Real-time monitor the user's health status through wearable devices and issue early warnings when abnormalities are detected.

[0100] The above system provided in this embodiment and the above method embodiment belong to the same concept. For the specific implementation process, please refer to the method embodiment and will not be elaborated here.

[0101] To implement the method of the embodiments of the present invention, the embodiments of the present invention also provide a computer program product. The computer program product includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to enable the computer device to execute the steps of the above method.

[0102] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of the present invention, the embodiments of the present invention further provide an electronic device (computer device). Specifically, in one embodiment, the computer device may be a terminal, and its internal structural diagram may be as shown in Figure 3 shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, it implements the method of any one of the above embodiments. The display screen A04 of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device A05 of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0103] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0104] The device provided by the embodiments of the present invention includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the method of any one of the above embodiments.

[0105] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0106] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0107] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks

[0109] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0110] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0111] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0112] It can be understood that the memory in the embodiments of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read-Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, ferromagnetic random access memory), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM, RandomAccess Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as a static random access memory (SRAM, Static Random Access Memory), a synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory), a dynamic random access memory (DRAM, Dynamic Random Access Memory), a synchronous dynamic random access memory (SDRAM, SynchronousDynamic Random Access Memory), a double data rate synchronous dynamic random access memory (DDRSDRAM, Double Data Rate Synchronous Dynamic Random Access Memory), an enhanced synchronous dynamic random access memory (ESDRAM, Enhanced Synchronous Dynamic Random Access Memory), a sync link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), a direct rambus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0113] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0114] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A cardiovascular and cerebrovascular health management method based on a deep learning and genetic environment interaction algorithm, characterized in that, The method includes: Obtaining the user's genetic gene data, physiological index data, environmental data, and lifestyle data; Constructing a cardiovascular and cerebrovascular disease risk prediction model based on a deep learning algorithm, and using the cardiovascular and cerebrovascular disease risk prediction model to process the genetic gene data, the physiological index data, the environmental data, and the lifestyle data, to obtain the prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model; Constructing a genetic-environment interaction analysis model using a genetic-environment interaction algorithm, and using the genetic-environment interaction analysis model to analyze the interaction between the genetic gene data and the environmental data, to obtain the evaluation result of the genetic gene data on the risk of cardiovascular and cerebrovascular diseases; Generating a personalized health management plan for the user according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases and the evaluation result of the genetic gene data on the risk of cardiovascular and cerebrovascular diseases; the personalized health management plan for the user includes lifestyle intervention suggestions.

2. The cardiovascular and cerebrovascular health management method based on the deep learning and genetic environment interaction algorithm according to claim 1, wherein Obtaining the user's genetic gene data, physiological index data, environmental data, and lifestyle data includes: Obtaining the user's genetic gene data, physiological index data, environmental data, and lifestyle data through the way of uploading by wearable devices and / or self-filling and uploading by the user.

3. The cardiovascular and cerebrovascular health management method based on the deep learning and genetic environment interaction algorithm according to claim 1, characterized in that After obtaining the user's genetic gene data, physiological index data, environmental data, and lifestyle data, the method further includes: Performing cleaning, standardization, and feature extraction operations on the genetic gene data, the physiological index data, the environmental data, and the lifestyle data.

4. The cardiovascular and cerebrovascular health management method based on the deep learning and genetic environment interaction algorithm according to claim 1, wherein, Using the cardiovascular and cerebrovascular disease risk prediction model to process the genetic gene data, the physiological index data, the environmental data, and the lifestyle data includes: Determining the weights corresponding to the genetic gene data, the physiological index data, the environmental data, and the lifestyle data respectively; Obtaining the scoring results output by the cardiovascular and cerebrovascular disease risk prediction model for the genetic gene data, the physiological index data, the environmental data, and the lifestyle data respectively; Calculating based on the scoring results and the weight values to obtain the prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model.

5. The cardiovascular and cerebrovascular health management method based on the deep learning and genetic environment interaction algorithm according to claim 4, wherein Calculating based on the scoring results and the weight values to obtain the prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model includes: Calculating based on the scoring results and the weight values using the following calculation formula: Risk Score = f(Risk Score') Risk Score' = wg*G + wp*P + we*E + wl*L Among them, Risk Score is the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, f() is a non-linear function, including logarithmic, exponential or piecewise functions; Risk Score' is the initial prediction result of the onset risk of cardiovascular and cerebrovascular diseases, wg is the weight value of genetic gene data; wp is the weight value of physiological index data; we is the weight value of environmental data; wl is the weight value of lifestyle data; G is genetic gene data; P is physiological index data; E is environmental data; L is lifestyle data.

6. The cardiovascular and cerebrovascular health management method based on the deep learning and genetic environment interaction algorithm according to claim 1, wherein After obtaining the prediction result of the onset risk of cardiovascular and cerebrovascular diseases output by the cardiovascular and cerebrovascular disease risk prediction model, the method further includes: Determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases.

7. The cardiovascular and cerebrovascular health management method based on the deep learning and genetic environment interaction algorithm according to claim 6, characterized in that, Determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases includes: Determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases by using the following calculation formula: Among them, Risk Score is the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, and T1 and T2 are judgment thresholds.

8. The cardiovascular and cerebrovascular health management method based on the deep learning and genetic environment interaction algorithm according to claim 7, characterized in that, After determining the corresponding risk level according to the prediction result of the onset risk of cardiovascular and cerebrovascular diseases, the method further includes: When it is determined that the risk level is high risk, an early warning signal is issued.

9. A cardiovascular health management system based on a deep learning and genetic environment interaction algorithm, characterized in that, Including: A processor and a memory for storing a computer program that can run on the processor; wherein, when the processor is used to run the computer program, it executes the steps of the method according to any one of claims 1 to 8.

10. A storage medium, in which a computer program is stored, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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