CN111785374A - Health condition analysis and prediction method and system based on big data - Google Patents
Health condition analysis and prediction method and system based on big data Download PDFInfo
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- 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
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Abstract
The invention discloses a health condition analysis and prediction method and a health condition analysis and prediction system based on big data, wherein the method comprises the following steps: firstly, establishing a health prediction model according to user basic parameters, user previous medical records, medicine purchase records, environmental parameters and user human body sign parameters; secondly, combining the health prediction model in the first step with an expert early warning knowledge base to obtain a health early warning index and a standard threshold value thereof; comparing the monitoring data with the health early warning index obtained in the second step and a standard threshold value thereof, and if the monitoring data exceeds the range of the standard threshold value, sending out a health early warning; according to the health condition analysis and prediction method and system based on big data, reference data of health analysis and prediction are more comprehensive, and prediction results are more accurate; the medical nursing bed is beneficial to prevention and medical care of diseases, so that a user can be diagnosed and treated in time, and the human health is guaranteed.
Description
Technical Field
The invention relates to a health condition analysis and prediction method and system, in particular to a health condition analysis and prediction method and system based on big data, and belongs to the technical field of health condition analysis and prediction of the big data.
Background
The medical industry is one of the traditional industries that earlier applied big data analytics. The five medical service fields comprise clinical business, a network platform, public health management, remote patient monitoring, new medicine development and the like, the depth and the breadth of application of big data are advanced, the medical effect and the user satisfaction are greatly improved through big data analysis, and big data health analysis prediction formed by the acquisition of human health data and the utilization of historical health information of a health correlation database is a main research and development direction in the medical field in the future.
Disclosure of Invention
In order to solve the problems, the invention provides a health condition analysis and prediction method and system based on big data, reference data of health analysis and prediction are more comprehensive, and prediction results are more accurate; the medical nursing bed is beneficial to prevention and medical care of diseases, so that a user can be diagnosed and treated in time, and the human health is guaranteed.
The invention relates to a health condition analysis and prediction method based on big data, which comprises the following steps:
firstly, establishing a health prediction model according to user basic parameters, user previous medical records, medicine purchase records, environmental parameters and user human body sign parameters;
secondly, combining the health prediction model in the first step with an expert early warning knowledge base to obtain a health early warning index and a standard threshold value thereof;
and thirdly, comparing the monitoring data with the health early warning index obtained in the second step and the standard threshold value thereof, and if the monitoring data exceeds the range of the standard threshold value, sending out health early warning.
Further, the basic parameters of the user comprise basic information parameters including the age, the sex, the BMI index, the occupation and the education level of the user, family genetic medical history information parameters of the user, and living and resting habits and eating habits information parameters of the user, which are acquired by adopting a questionnaire mode.
Further, the specific operation steps of the first step are as follows: the basic user parameters, the past medical records, the medicine purchasing records, the environmental parameters and the human body physical sign parameters of the user are uploaded to a database of a big data server, and a data analysis processing module of the big data server analyzes the basic user causes, family history causes, the lifestyle factors, the dietary habit causes, the environmental causes and the human body physical sign causes which are corresponding to the user with the same medical records in the whole database, so that the same causes of the corresponding medical records are counted, and a health prediction model is established.
Further, the expert early warning knowledge base comprises various disease diagnosis principles, a disease early warning index range and a corresponding emergency scheme during disease early warning.
Further, the specific operation steps of the second step are as follows: and (3) combining the user basic inducement, family history inducement, user living work and rest inducement, diet habit inducement, environment inducement and human body physical sign inducement of the health prediction model in the first step with the disease diagnosis principle and the early warning index range of the corresponding diseases of the expert early warning knowledge base to obtain health early warning indexes and standard threshold values thereof, and feeding back the health early warning indexes and the standard threshold values to the emergency scheme of the corresponding diseases of the user.
Further, the monitoring data comprise human body sign parameters and sleep quality parameters acquired through the wearable device, and environmental weather parameters acquired by the user terminal through the internet.
Still further, blood pressure acquisition unit, body temperature acquisition unit, heart rate acquisition unit and sleep quality acquisition unit are integrated in the wearable equipment.
The health condition analysis and prediction system based on big data comprises wearable equipment for acquiring human body sign parameters and sleep quality parameters of a user; the user terminal is used for receiving the human body sign parameters and the sleep quality parameters, uploading the information parameters to the big data server, accessing and checking the health prediction result fed back by the big data server, and is in communication connection with the wearable device; the big data server is used for analyzing and managing the received information parameters, providing health analysis prediction for the user and is in communication connection with the user terminal; the big data server comprises a data receiving module which is used for receiving information parameters uploaded by the user terminal and is in communication connection with the user terminal; and a questionnaire registration module for collecting basic information parameters of the user; and a medical record and medicine registration module for collecting the past medical record and medicine purchasing record of the user; the climate recording module is used for acquiring the environmental weather of the user during previous morbidity; and a database which is used for storing questionnaire registration information, medical record and medicine registration information, disease period climate information, disease period human body sign information and sleep quality information, and is respectively in communication connection with the questionnaire registration module, the medical record and medicine registration module, the climate recording module and the data receiving module; the system is used for analyzing and processing questionnaire registration information, medical record and medicine registration information, climate information during disease attack, human body sign information during disease attack and sleep quality information in a database, establishing a health prediction model of a corresponding medical record, and providing a corresponding health early warning index and a standard threshold value thereof by combining the health prediction model and a background expert early warning knowledge base; meanwhile, the monitoring data is compared with the health early warning index and the standard threshold value thereof for analysis, and the data analysis processing module is in communication connection with the database; the data feedback module is used for performing health early warning feedback according to the comparison result of the monitoring data and the health early warning index and the standard threshold value thereof, giving an emergency scheme and reminding a user to seek medical treatment in time and is in communication connection with the data analysis processing module; and the data feedback module is in communication connection with the user terminal through a GPS.
As a preferred embodiment, the system also comprises a temporary input module for inputting the daily living state and the dietary condition of the user.
Compared with the prior art, the health condition analysis and prediction method and system based on big data of the invention take the basic information of users (age, sex, occupation, family history, life and eating habits and the like which can become factors of disease inducement), past medical records, medicine purchasing records, weather conditions and human body sign parameters during disease incidence as basic data of health analysis and prediction, and give out health early warning indexes and standard thresholds thereof by combining with expert early warning indexes, so that the reference data of health analysis and prediction is more comprehensive, and the prediction result is more accurate; the user passes through wearable equipment real-time supervision and gathers human sign parameter to acquire the environmental weather variation parameter by intelligent user terminal, compare monitoring parameter and healthy early warning index and standard threshold value thereof, if monitoring data surpasss standard threshold value scope, then send the healthy early warning, give emergent scheme, and remind the user in time to seek medical advice, help the prevention of disease and medical care, make the user can obtain timely diagnosis and treatment, ensure human health.
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FIG. 1 is a block diagram showing the structure of embodiment 1 of the present invention.
FIG. 2 is a block diagram showing the structure of embodiment 2 of the present invention.
The parts in the drawings are marked as follows: the system comprises 1-wearable equipment, 2-user terminals, 3-big data servers, 31-a data receiving module, 32-a questionnaire registration module, 33-a medical record and medicine registration module, 34-a climate recording module, 35-a database, 36-a data analysis processing module, 37-a data feedback module, 38-an expert early warning knowledge base, 39-a temporary recording module and a 4-GPS positioning system.
Detailed Description
Example 1:
the invention relates to a health condition analysis and prediction method based on big data, which comprises the following steps:
firstly, establishing a health prediction model according to user basic parameters, user previous medical records, medicine purchase records, environmental parameters and user human body sign parameters;
secondly, combining the health prediction model in the first step with an expert early warning knowledge base to obtain a health early warning index and a standard threshold value thereof;
and thirdly, comparing the monitoring data with the health early warning index obtained in the second step and the standard threshold value thereof, and if the monitoring data exceeds the range of the standard threshold value, sending out health early warning.
The basic user parameters comprise basic information parameters including the age, the sex, the BMI index, the occupation and the education level of the user, family genetic medical history information parameters of the user, and living and resting habits and eating habits information parameters of the user, which are acquired by adopting a questionnaire mode.
The specific operation steps of the first step are as follows: the basic user parameters, the past medical records, the medicine purchasing records, the environmental parameters and the human body physical sign parameters of the user are uploaded to a database of a big data server, and a data analysis processing module of the big data server analyzes the basic user causes, family history causes, the lifestyle factors, the dietary habit causes, the environmental causes and the human body physical sign causes which are corresponding to the user with the same medical records in the whole database, so that the same causes of the corresponding medical records are counted, and a health prediction model is established.
The expert early warning knowledge base comprises various disease diagnosis principles, a disease early warning index range and a corresponding emergency scheme during disease early warning.
The second step comprises the following specific operation steps: and (3) combining the user basic inducement, family history inducement, user living work and rest inducement, diet habit inducement, environment inducement and human body physical sign inducement of the health prediction model in the first step with the disease diagnosis principle and the early warning index range of the corresponding diseases of the expert early warning knowledge base to obtain health early warning indexes and standard threshold values thereof, and feeding back the health early warning indexes and the standard threshold values to the emergency scheme of the corresponding diseases of the user.
The monitoring data comprise human body sign parameters and sleep quality parameters acquired through the wearable device and environmental weather parameters acquired by the user terminal through the Internet.
Blood pressure acquisition unit, body temperature acquisition unit, heart rate acquisition unit and sleep quality acquisition unit are integrated in the wearable equipment.
The big data based health condition analysis and prediction system as shown in fig. 1 comprises a wearable device 1 for acquiring human body sign parameters and sleep quality parameters of a user; the user terminal 2 is used for receiving the human body sign parameters and the sleep quality parameters, uploading the information parameters to the big data server, accessing and checking the health prediction result fed back by the big data server, and is in communication connection with the wearable device; and a big data server 3 used for analyzing and managing the received information parameters, providing health analysis prediction for users and being in communication connection with the user terminal; the big data server 3 comprises a data receiving module 31 which is used for receiving information parameters uploaded by the user terminal and is in communication connection with the user terminal; and a questionnaire registration module 32 for collecting basic information parameters of the user; and a medical record and medicine registration module 33 for collecting the past medical records and medicine purchase records of the user; a climate recording module 34 for acquiring the environmental weather of the user during previous morbidity; and a database 35 for storing questionnaire registration information, medical record and drug registration information, climate information during disease onset, and human body sign information and sleep quality information during disease onset, and respectively connected with the questionnaire registration module, the medical record and drug registration module, the climate recording module and the data receiving module in a communication manner; and is used for analyzing and processing questionnaire registration information, medical record and medicine registration information, climate information during disease onset, human body sign information during disease onset and sleep quality information in the database, establishing a health prediction model of the corresponding medical record, and providing corresponding health early warning indexes and standard thresholds thereof by combining the health prediction model and the expert early warning knowledge base 38 of the background; meanwhile, the data analysis processing module 36 compares and analyzes the monitoring data with the health early warning index and the standard threshold thereof, and is in communication connection with the database; and a data feedback module 37 for performing health early warning feedback according to the comparison result of the monitoring data and the health early warning index and the standard threshold thereof, giving an emergency scheme, reminding the user to seek medical treatment in time, and being in communication connection with the data analysis processing module; the data feedback module 37 is in communication connection with the user terminal 2 through the GPS positioning system 4.
Example 2:
the big data-based health condition analyzing and predicting system shown in fig. 2 has a structure substantially the same as that of embodiment 1, and further includes a temporary entry module 39 for inputting the daily living state and dietary conditions of the user.
According to the health condition analysis and prediction method and system based on big data, the basic user parameters including the age, sex, BMI index, occupation, education level, family genetic medical history, working and resting habits, eating habits and drug allergy of the user are used as health early warning reference data; taking the previous medical records and possible complications of the user as basic reference data of health early warning; the medicine types recorded by medicine purchasing and the purchasing times of various medicines are counted, and taken as one of health early warning judgment conditions, and a corresponding medicine taking suggestion can be given in subsequent health early warning; the environmental weather change in the past disease attack period is used as one of health early warning judgment conditions, whether the disease occurrence is related to seasonal environmental change is judged through the environmental temperature, humidity, air pressure change and the like in the disease attack period, and therefore the disease caused by the seasonal change is predicted, diagnosed and treated; the change rule of the human body signs in the past morbidity period is obtained by counting and analyzing the human body sign parameters in the past morbidity period, so that a judgment basis is provided for human body health prediction;
basic information (factors such as age, sex, occupation, life and dietary habits which can become causes of diseases), previous medical records, climate conditions during medicine purchase recording and morbidity and human body sign parameters are used as basic data for health analysis and prediction, and a health early warning index and a standard threshold value thereof are given in combination with an expert early warning index, so that reference data for health analysis and prediction are more comprehensive, and a prediction result is more accurate; the user passes through wearable equipment real-time supervision and gathers human sign parameter to acquire the environmental weather variation parameter by intelligent user terminal, compare monitoring parameter and healthy early warning index and standard threshold value thereof, if monitoring data surpasss standard threshold value scope, then send the healthy early warning, give emergent scheme, and remind the user in time to seek medical advice, help the prevention of disease and medical care, make the user can obtain timely diagnosis and treatment, ensure human health.
The above-described embodiments are merely preferred embodiments of the present invention, and all equivalent changes or modifications of the structures, features and principles described in the claims of the present invention are included in the scope of the present invention.
Claims (9)
1. A big data based health condition analysis and prediction method is characterized by comprising the following steps:
firstly, establishing a health prediction model according to user basic parameters, user previous medical records, medicine purchase records, environmental parameters and user human body sign parameters;
secondly, combining the health prediction model in the first step with an expert early warning knowledge base to obtain a health early warning index and a standard threshold value thereof;
and thirdly, comparing the monitoring data with the health early warning index obtained in the second step and the standard threshold value thereof, and if the monitoring data exceeds the range of the standard threshold value, sending out health early warning.
2. The big-data based health analyzing and predicting method according to claim 1, wherein the user basic parameters include basic information parameters including user age, gender, BMI index, occupation and education level, and user family genetic history information parameters, and user lifestyle habits and dietary habits information parameters, which are collected by using a questionnaire.
3. The big-data-based health analysis and prediction method according to claim 1, wherein the specific operation steps of the first step are as follows: the basic user parameters, the past medical records, the medicine purchasing records, the environmental parameters and the human body physical sign parameters of the user are uploaded to a database of a big data server, and a data analysis processing module of the big data server analyzes the basic user causes, family history causes, the lifestyle factors, the dietary habit causes, the environmental causes and the human body physical sign causes which are corresponding to the user with the same medical records in the whole database, so that the same causes of the corresponding medical records are counted, and a health prediction model is established.
4. The big-data-based health condition analysis and prediction method according to claim 1, wherein the expert early warning knowledge base comprises various disease diagnosis principles, disease early warning index ranges and corresponding emergency schemes during disease early warning.
5. The big-data based health analysis and prediction method according to claim 1, wherein the second step comprises the following specific steps: and (3) combining the user basic inducement, family history inducement, user living work and rest inducement, diet habit inducement, environment inducement and human body physical sign inducement of the health prediction model in the first step with the disease diagnosis principle and the early warning index range of the corresponding diseases of the expert early warning knowledge base to obtain health early warning indexes and standard threshold values thereof, and feeding back the health early warning indexes and the standard threshold values to the emergency scheme of the corresponding diseases of the user.
6. The big-data-based health condition analysis and prediction method according to claim 1, wherein the monitoring data includes human body sign parameters and sleep quality parameters collected by the wearable device, and environmental weather parameters acquired by the user terminal through the internet.
7. The big-data-based health analysis and prediction method according to claim 6, wherein a blood pressure collection unit, a body temperature collection unit, a heart rate collection unit, and a sleep quality collection unit are integrated in the wearable device.
8. A health condition analysis and prediction system based on big data is characterized by comprising a wearable device used for collecting human body sign parameters and sleep quality parameters of a user; the user terminal is used for receiving the human body sign parameters and the sleep quality parameters, uploading the information parameters to the big data server, accessing and checking the health prediction result fed back by the big data server, and is in communication connection with the wearable device; the big data server is used for analyzing and managing the received information parameters, providing health analysis prediction for the user and is in communication connection with the user terminal; the big data server comprises a data receiving module which is used for receiving information parameters uploaded by the user terminal and is in communication connection with the user terminal; and a questionnaire registration module for collecting basic information parameters of the user; and a medical record and medicine registration module for collecting the past medical record and medicine purchasing record of the user; the climate recording module is used for acquiring the environmental weather of the user during previous morbidity; and a database which is used for storing questionnaire registration information, medical record and medicine registration information, disease period climate information, disease period human body sign information and sleep quality information, and is respectively in communication connection with the questionnaire registration module, the medical record and medicine registration module, the climate recording module and the data receiving module; the system is used for analyzing and processing questionnaire registration information, medical record and medicine registration information, climate information during disease attack, human body sign information during disease attack and sleep quality information in a database, establishing a health prediction model of a corresponding medical record, and providing a corresponding health early warning index and a standard threshold value thereof by combining the health prediction model and a background expert early warning knowledge base; meanwhile, the monitoring data is compared with the health early warning index and the standard threshold value thereof for analysis, and the data analysis processing module is in communication connection with the database; the data feedback module is used for performing health early warning feedback according to the comparison result of the monitoring data and the health early warning index and the standard threshold value thereof, giving an emergency scheme and reminding a user to seek medical treatment in time and is in communication connection with the data analysis processing module; and the data feedback module is in communication connection with the user terminal through a GPS.
9. The big data-based health analysis and prediction system of claim 8, further comprising a temporary entry module for entering the user's daily living status and dietary status.
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| CN112669972A (en) * | 2020-12-30 | 2021-04-16 | 华南师范大学 | Chinese medicine personal risk integration and integration cooperative prediction method based on big data deep learning |
| CN112704482A (en) * | 2020-12-17 | 2021-04-27 | 北京智康人人科技有限公司 | Heart and brain detection early warning method, platform and computer readable storage medium thereof |
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| CN114179107A (en) * | 2021-12-28 | 2022-03-15 | 北京北控京奥建设有限公司 | Intelligent health management system and method for desktop robot |
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Application publication date: 20201016 |