CN114999647A - Intelligent health monitoring method and system based on big data - Google Patents
Intelligent health monitoring method and system based on big data Download PDFInfo
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Abstract
The invention belongs to the technical field of health monitoring, and particularly discloses an intelligent health monitoring method and system based on big data, wherein the system comprises: the physiological index acquisition module is used for acquiring various physiological index data of a human body; the database is used for recording various physiological index data of the human body; the analysis module is used for judging the health condition of the human body according to the human body physiological index data in the database and a preset judgment standard; the big data grouping module is used for comparing the parameters in the database with the big data, grouping the big data according to the physique of the human body and adjusting the judgment standard of the analysis module according to the grouping condition; the physiological data of the human body is compared with the big data, the monitored personnel are divided into groups similar to the physique of the monitored personnel, and then the judgment standard applicable to the physique is adopted to analyze the physiological index data, so that the health condition of the human body can be judged more accurately and sensitively.
Description
Technical Field
The invention relates to the technical field of health monitoring, in particular to an intelligent health monitoring method and system based on big data.
Background
Along with the rapid development of physiological detection technology and internet, health monitoring equipment such as sphygmomanometer, blood glucose meter is widely used, and the acquisition of human health data is more and more convenient, and through synchronizing the health data to the data platform, can convenient and fast monitor the health condition of human body, and then discover in time whether the human body is sick, improve the treatment effect and the treatment cycle of disease, effectively reduced the emergence of the disease aggravation problem that the disease delays to cause.
The current health monitoring system is through the monitoring to each item of physiological data of human body to compare each item of data with the standard range that corresponds, and then judge the health status of human body, and is concrete, when data is in the standard range that corresponds, the suggestion is by monitoring personnel health normal, and when data is not in the standard range that corresponds, the system can in time remind by monitoring personnel to notice this unusual, and then help monitoring personnel in time discover health status and in time take corresponding treatment, realize the monitoring process to human health parameter.
However, in the early stage of many diseases, the variation of physiological parameters of human body is not particularly obvious, and meanwhile, the standard setting of physiological parameters has a wide range due to different constitutions of different people; therefore, when the existing health monitoring system is adopted for monitoring, the physiological parameters are compared with the standard ranges one by one, and firstly, the tiny fluctuation of the physiological parameters is difficult to be found in a large parameter standard range in time, so that the problem of insufficient sensitivity exists for disease discovery, and secondly, the physiological parameters of people with different constitutions have differences, so that although the physiological parameters of the monitored people belong to the standard ranges and the data of the physiological parameters are judged to be normal, the physiological parameters of the people with the different constitutions possibly do not belong to the normal ranges corresponding to the people with the different constitutions, so that the fixed standard ranges influence the accuracy of health judgment of the monitored people, and further the potential health hazards of the human body cannot be found in advance.
Disclosure of Invention
The invention aims to provide an intelligent health monitoring method and system based on big data, and the intelligent health monitoring method and system can be used for solving the following technical problems:
how to improve the sensitivity and accuracy of human health monitoring.
The purpose of the invention can be realized by the following technical scheme:
a big data based intelligent health monitoring system, the system comprising:
the physiological index acquisition module is used for acquiring various physiological index data of a human body;
the database is used for recording various physiological index data of the human body;
the analysis module is used for judging the health condition of the human body according to the human body physiological index data in the database and a preset judgment standard;
and the big data grouping module is used for comparing the parameters in the database with the corresponding parameter ranges of all the groups in the big data respectively and determining the group, and adjusting the judgment standard of the analysis module according to the parameter range of the group.
Obviously, the physiological index data are analyzed by adopting the judgment standard applicable to the grouped physique, so that the health condition of the human body can be judged more accurately and sensitively, and the monitored personnel can be helped to discover the diseases or the risks of the diseases existing in the body in time.
In some embodiments, the system further comprises an environment data acquisition module for acquiring environment data of the human body and living habit data of the human body;
the database is also used for recording the environmental data of the human body and the living habit data of the human body.
The environmental data of the human body and the living habit data of the human body can be further refined in the grouping process, and the monitoring sensitivity is further improved.
In some embodiments, the analysis module operates as follows:
marking the physiological indexes as M respectively 1 、M 2 、…M i I is 1, 2, …, n represents the total number of terms of the physical index;
respectively corresponding each body index to a corresponding preset range interval [ M ] 1x ，M 1y ]、[M 2x ，M 2y ]、…[M ix ，M iy ]And (3) carrying out comparison:
when in use
Then M will be i Marking the corresponding physiological index items;
and comparing the physiological index items of all the marks with physiological index abnormal items corresponding to different diseases, and judging the types of the diseases according to the superposition condition.
In some embodiments, the big data packet module operates as follows:
comparing the data acquired by the environmental data acquisition module with the big data to determine an initial group matched with the user;
acquiring data of at least two groups of physiological indexes in a normal state, respectively comparing each physiological index with the corresponding physiological index of each group in the initial group, and selecting one group with the highest proximity degree as a selection group;
reducing the preset range [ M ] according to the physiological index range corresponding to the selected group ix ，M iy ]。
In some embodiments, the system further comprises a disease risk prediction module;
the disease risk prediction module predicts the following steps:
acquiring at least two human body physiological indexes separated by a specific time period;
respectively calculating the variable quantity of each human body physiological index, and comparing the variable quantity with the threshold value corresponding to each index:
when the variation is larger than the threshold value, marking the physiological index item corresponding to the variation;
and comparing the physiological index items of all the marks with physiological index abnormal items corresponding to different diseases, and predicting the types of the diseases according to the superposition condition.
The abnormality of the physiological index data is compared with physiological index abnormal items corresponding to different diseases, and the type of the disease can be roughly judged according to the superposition condition.
Further, the corresponding physical examination items are recommended according to the predicted disease category.
In some embodiments, the system further comprises an online diagnostic module;
and the online diagnosis module selects a doctor with the highest coincidence degree with the disease to make an appointment according to the type of the disease.
Further, the online diagnosis module selects a doctor by the steps of:
determining a main disease label A, a secondary disease label B and a plurality of other disease labels C according to the contact ratio of the marked physiological index items and physiological index abnormal items corresponding to different diseases;
establishing an indication disease label library L corresponding to each doctor according to a formula Co ═ gamma 1 A+γ 2 B+γ 3 (C 1 +C 2 +…+C n ) Calculating the disease coincidence degree Co of each doctor and the user;
wherein a represents the primary disorder label, if a ∈ L, then a ═ 1, otherwise a ═ 0, B represents the secondary disorder label, if B ∈ L, then B ═ 1, otherwise B ∈ 0, C n Denotes the other disorder label, n denotes the number of all other disorder labels, if C n E is L, then C n 1, otherwise C n ＝0，γ 1 、γ 2 And gamma 3 Is a predetermined scale factor, and γ 1 ＞γ 2 ＞γ 3 ；
And selecting the doctor with the highest disease coincidence degree Co with the user for reservation.
The diagnosticians are selected according to the disease coincidence degree, so that the doctors with the most similar fit to the corresponding diseases can be selected, and the specialty and the accuracy of diagnosis of the doctors are guaranteed.
A big-data based intelligent health monitoring, the method comprising:
s1, collecting physiological index data of the human body, environmental data of the human body and life habit data of the human body;
s2, judging the health condition of the human body according to the human body physiological index data and a preset judgment standard;
s3, comparing the physiological index data, the environmental data and the living habit data of the human body with the big data, grouping the human body constitution, and adjusting the judgment standard of the analysis module according to the grouping condition.
The invention has the beneficial effects that:
(1) according to the invention, the physiological data of the human body is compared with the big data, so that the monitored personnel are divided into groups similar to the constitution of the monitored personnel, and then the physiological index data are analyzed by adopting the judgment standard applicable to the constitution, so that the health condition of the human body can be judged more accurately and sensitively.
(2) The invention further divides the system according to the collected environmental data and the living habits of the human body, can improve the refinement degree of grouping, and further improves the monitoring sensitivity.
(3) According to the invention, the disease risk of the human body can be preliminarily predicted through the change of the physiological indexes, and the disease risk can be judged before the physiological indexes exceed the standard range, so that the timely diagnosis and treatment of the detected personnel can be facilitated.
(4) The invention can reduce the inconvenience caused by the physical examination of the whole body of the human body, can also carry out further judgment on the risk of diseases in a targeted manner and ensures the health of the human body.
(5) According to the invention, the diagnosing doctor is selected according to the disease coincidence degree, and when the coincidence degree is higher, the condition of the professional field of the doctor is more similar to that of the diagnosed person, so that the doctor with the largest disease coincidence degree of the user is selected for reservation, and more accurate diagnosis can be made on the disease.
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The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a big data based intelligent health monitoring system according to the present invention;
fig. 2 is a flowchart illustrating steps of a method for intelligent health monitoring based on big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in an embodiment, an intelligent health monitoring system based on big data is provided, the system includes a physiological index collecting module for collecting physiological index data of a human body, the collecting mode can be obtained by a mode actively input by a user and can also be synchronized by a related physiological detecting instrument, the collected physiological index data includes but is not limited to basic physiological data of the human body, such as blood pressure, blood sugar, heart rate, body temperature, etc., and also includes parameters of blood obtained during a physical examination; the system comprises a database, an analysis module, a database and a data base, wherein the database records various physiological index data of a human body, and the analysis module analyzes the health of the human body in time, particularly, when the system of the embodiment is used for the first time, the physiological index data of the human body can be judged according to a preset judgment standard, the preset judgment standard is a set of judgment standards applicable to all people, and after the system of the embodiment is used for multiple times, the database stores data of the same person at different time points, so that a big data grouping module in the system of the embodiment can compare the physiological data of the human body with big data, further divides monitored personnel into groups similar to the constitution of the monitored personnel, then analyzes the physiological index data by adopting the judgment standard applicable to the constitution, can judge the health condition of the human body more accurately and sensitively, and further performs treatment as early as possible, various problems caused by the aggravation of the illness are avoided.
In addition, as shown in fig. 1, the system in this embodiment further includes an environmental data collection module, and the environmental data collection module can collect environmental data where a human body is located and lifestyle data of the human body, specifically, the environmental data includes but is not limited to average temperature, pollutant status, and the lifestyle includes but is not limited to smoking or drinking or the like, so that the system is further divided according to the collected environmental data and lifestyle of the human body, the grouping refinement degree can be improved, and the monitoring sensitivity is further improved.
It should be noted that the big data in this embodiment records groups divided according to the constraint conditions of different constitutions, different regions, different ages, different habits, and the like, and each group has a corresponding detection standard.
As an embodiment of the present invention, this embodiment provides a specific working manner of an analysis module, specifically, firstly, the physiological indexes are respectively labeled as M 1 、M 2 、…M i I represents any one, i is 1, 2, …, n represents the total number of terms of the physical index; then, each body index is respectively corresponding to the corresponding preset range interval [ M 1x ，M 1y ]、[M 2x ，M 2y ]、…[M ix ，M iy ]And (3) carrying out comparison: when in use
When it is, M will be used to indicate that there is a problem with the physiological index i Marking the corresponding physiological index items; wherein M is ix Minimum value, M, of a range corresponding to the ith physiological index iy Represents the maximum value of the range corresponding to the i-th physiological index.
Finally, all the marked physiological index items are counted and compared with physiological index abnormal items corresponding to different diseases in the big data, the basic physiological index parameters of the human body can be changed in different correspondences in the attack process of the different diseases, therefore, the types of the diseases can be roughly judged according to the superposition condition through comparing the abnormality of the physiological index data with the physiological index abnormal items corresponding to the different diseases, obviously, the disease with the highest superposition degree is the judged disease type, and when the disease with the highest superposition degree is more than one, all the diseases are used as the judged disease type.
In the present embodiment, the big data records various common diseases and basic physiological parameter abnormalities corresponding to the various diseases.
As an embodiment of the present invention, this embodiment provides a specific working method of a big data grouping module, specifically, firstly, data collected by an environmental data collection module is compared with the big data, a detected person is primarily limited to a plurality of groups of primary groups matched with the living environment and living habits of the detected person, secondly, data in a normal state of two groups of physiological indexes is obtained, because the single group of data has an influence of data error, a group closest to the physique of the detected person can be accurately selected as a selection group according to the data proximity degree by comparing at least two groups of physiological index data with corresponding physiological indexes of each group in the primary groups, and finally, a preset range [ M ] is narrowed and adjusted according to the physiological index range corresponding to the selection group ix ，M iy ]Obviously, the narrowed range interval can more accurately and sensitively judge whether physiological indexes have deviation, and further judge the health condition of the human body.
It should be noted that, because the preset range interval needs to be adapted to the requirements of different people, the adjustment process is to further define the range interval.
Further, the above proximity degree refers to the number of the indexes respectively overlapping with the corresponding index range intervals of each group, and obviously, the greater the number of the indexes overlapping, the higher the proximity degree.
As an embodiment of the present invention, the system further includes a disease risk prediction module, the disease risk prediction module can predict a disease according to a change of the physiological parameter, specifically, the step of predicting by the disease risk prediction module is: firstly, acquiring at least two human body physiological indexes separated by a specific time period; and then calculating the variation of each human body physiological index, comparing the variation with the threshold corresponding to each index, obviously, when the variation is larger than the threshold, the physiological index is abnormal, so the physiological index item corresponding to the variation is marked, finally, the physiological index item corresponding to all the marks is compared with the physiological index abnormal item corresponding to different diseases, the type of the diseases is predicted according to the superposition condition, obviously, the disease risk of the human body can be preliminarily predicted through the variation of the physiological index, the disease risk can be judged before the physiological index exceeds the standard range, and further, the timely diagnosis and treatment of the detected personnel can be facilitated.
It should be noted that, the judgment of the variation of the physiological index in the present embodiment is performed in a state where the data of the physiological index in both time periods are normal, and obviously, when the physiological index is abnormal, it has been described that there is a problem in the body and there is no need for prediction.
Further, the above proximity refers to the number of overlapping of each index with the physiological index abnormal item corresponding to different diseases, and obviously, the greater the number of overlapping indexes, the higher the proximity.
Furthermore, corresponding physical examination items can be recommended according to the predicted disease types, the disease risk of the human body can be found in time through periodic physical examination, however, the overall physical examination process needs higher time cost and money cost, and therefore, the whole body examination is difficult to be frequently performed.
As an embodiment of the present invention, the system in this embodiment further includes an online diagnosis module, when the health of the human body is monitored, when it is monitored that a disease exists in the human body but the type of the disease is difficult to be determined, a professional doctor is required to perform a specific diagnosis, and the online diagnosis system can facilitate the doctor to diagnose the patient.
Further, the embodiment provides the specific steps of the online diagnosis module selecting the doctor: firstly, according to the contact ratio of the marked physiological index item and the physiological index abnormal item corresponding to different diseases, a main disease label A, a secondary disease label B and a plurality of other disease labels C are determined; then, a corresponding main disease label library L of each doctor is established, and the formula Co is equal to gamma 1 A+γ 2 B+γ 3 (C 1 +C 2 +…+C n ) Calculating the disease coincidence degree Co of each doctor and the user; obviously, the higher the coincidence degree is, the closer the professional field of the doctor is to the disease of the diagnosed person, so that the doctor with the highest disease coincidence degree Co with the user is selected to make an appointment, and the disease can be diagnosed more accurately.
It should be noted that the setting of the primary disease label a, the secondary disease label B, and the other disease labels C can adopt different weights according to the disease similarity, so as to further improve the degree of engagement selected by the doctor, and ensure accurate judgment and monitoring of the health condition of the user.
Wherein a represents the primary disorder label, if a ∈ L, then a ═ 1, otherwise a ═ 0, B represents the secondary disorder label, if B ∈ L, then B ═ 1, otherwise B ∈ 0, C n Representing other disorder labels, n represents the number of all other disorder labels, if C n E is L, then C n 1, otherwise C n ＝0，γ 1 、γ 2 And gamma 3 Is a predetermined scale factor, and γ 1 ＞γ 2 ＞γ 3 。
Referring to fig. 2, in an embodiment of the present invention, a big data based intelligent health monitoring is provided, the method includes: s1, collecting physiological index data of the human body, environmental data of the human body and life habit data of the human body; s2, judging the health condition of the human body according to the human body physiological index data and a preset judgment standard; s3, comparing the physiological index data, the environmental data and the living habit data of the human body with the big data, grouping the human body constitution, and adjusting the judgment standard of the analysis module according to the grouping condition; the physiological data of the human body is compared with the big data, the monitored personnel are divided into groups similar to the constitution of the monitored personnel, and then the judgment standard applicable to the constitution is adopted to analyze the physiological index data, so that the health condition of the human body can be judged more accurately and sensitively; meanwhile, the further division is carried out according to the collected environmental data and the living habits of the human body, the grouping refinement degree can be improved, and the monitoring sensitivity is further improved.
While one embodiment of the present invention has been described in detail, the description is only a preferred embodiment of the present invention and should not be taken as limiting the scope of the invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (9)
1. An intelligent health monitoring system based on big data, the system comprising:
the physiological index acquisition module is used for acquiring various physiological index data of a human body;
the database is used for recording various physiological index data of the human body;
the analysis module is used for judging the health condition of the human body according to the human body physiological index data in the database and a preset judgment standard;
and the big data grouping module is used for comparing the parameters in the database with the corresponding parameter ranges of all the groups in the big data respectively and determining the group, and adjusting the judgment standard of the analysis module according to the parameter range of the group.
2. The intelligent health monitoring system based on big data as claimed in claim 1, further comprising an environmental data collection module for collecting environmental data of human body and living habit data of human body;
the database is also used for recording the environmental data of the human body and the living habit data of the human body.
3. The big data based intelligent health monitoring system as claimed in claim 2, wherein the analysis module operates by the steps of:
marking the physiological indexes as M respectively 1 、M 2 、…M i I is 1, 2, …, n represents the total number of terms of the physical index;
respectively corresponding each body index to a corresponding preset range interval [ M ] 1x ，M 1y ]、[M 2x ，M 2y ]、…[M ix ，M iy ]And (3) carrying out comparison:
when in use
Then M is added i Marking the corresponding physiological index items;
and comparing the physiological index items of all the marks with physiological index abnormal items corresponding to different diseases, and judging the types of the diseases according to the superposition condition.
4. The intelligent big-data-based health monitoring system according to claim 3, wherein the big-data grouping module operates as follows:
comparing the data acquired by the environmental data acquisition module with the big data to determine an initial group matched with the user;
acquiring data of at least two groups of physiological indexes in a normal state, respectively comparing each physiological index with the corresponding physiological index of each group in the initial group, and selecting one group with the highest proximity degree as a selection group;
reducing the preset range [ M ] according to the physiological index range corresponding to the selected group ix ，M iy ]。
5. The big data based intelligent health monitoring system as claimed in claim 3, further comprising a disease risk prediction module;
the disease risk prediction module predicts the following steps:
acquiring at least two human body physiological indexes separated by a specific time period;
respectively calculating the variable quantity of each human body physiological index, and comparing the variable quantity with the threshold value corresponding to each index:
when the variation is larger than the threshold value, marking the physiological index item corresponding to the variation;
and comparing the physiological index items of all the marks with physiological index abnormal items corresponding to different diseases, and predicting the types of the diseases according to the superposition condition.
6. The big data based intelligent health monitoring system as claimed in claim 5, wherein the corresponding physical examination items are recommended according to the predicted disease category.
7. The big data based intelligent health monitoring system as claimed in claim 3, further comprising an online diagnostic module;
and the online diagnosis module selects a doctor with the highest coincidence degree with the disease according to the type of the disease to make an appointment.
8. The big data based intelligent health monitoring system as claimed in claim 7, wherein the online diagnosis module selects a doctor by the steps of:
determining a main disease label A, a secondary disease label B and a plurality of other disease labels C according to the contact ratio of the marked physiological index items and physiological index abnormal items corresponding to different diseases;
establishing an indication disease label library L corresponding to each doctor according to a formula Co ═ gamma 1 A+γ 2 B+γ 3 (C 1 +C 2 +…+C n ) Calculating the disease coincidence degree Co of each doctor and the user;
wherein, A represents a primary disorder label, if A belongs to L, then A equals 1, otherwise A equals 0, B represents a secondary disorder label, if B belongs to L, then B equals 1, otherwiseThen B is 0, C n Representing other disorder labels, n represents the number of all other disorder labels, if C n E is L, then C n 1, otherwise C n ＝0，γ 1 、γ 2 And gamma 3 Is a predetermined scale factor, and γ 1 ＞γ 2 ＞γ 3 ；
And selecting the doctor with the highest disease coincidence degree Co with the user for reservation.
9. An intelligent health monitor based on big data, the method comprising:
s1, collecting physiological index data of the human body, environmental data of the human body and life habit data of the human body;
s2, judging the health condition of the human body according to the human body physiological index data and a preset judgment standard;
s3, comparing the physiological index data, the environmental data and the living habit data of the human body with the big data, grouping the human body constitution, and adjusting the judgment standard of the analysis module according to the grouping condition.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116313132A (en) * | 2023-05-24 | 2023-06-23 | 四川省医学科学院·四川省人民医院 | Medical management system for chronic diseases |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116313132A (en) * | 2023-05-24 | 2023-06-23 | 四川省医学科学院·四川省人民医院 | Medical management system for chronic diseases |
| CN116313132B (en) * | 2023-05-24 | 2023-08-11 | 四川省医学科学院·四川省人民医院 | Medical management system for chronic diseases |
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