CN107436997A - The analysis system and method for a kind of physiological data - Google Patents
The analysis system and method for a kind of physiological data Download PDFInfo
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- CN107436997A CN107436997A CN201710533679.4A CN201710533679A CN107436997A CN 107436997 A CN107436997 A CN 107436997A CN 201710533679 A CN201710533679 A CN 201710533679A CN 107436997 A CN107436997 A CN 107436997A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
Abstract
The invention discloses a kind of analysis system of physiological data, the analysis system of the physiological data includes：Data acquisition module, data memory module, data analysis module, data outputting module；Data acquisition module, the data acquisition module is used to gather physiological data, the physiological data is derived from the data sources related to physiologic information event such as personal health archives, physical examination report, questionnaire information, statistical information, and the personal health archives include audit report, the medical informations such as record of seeing a doctor；Data memory module, the data memory module store to physiological data, have each physiological data weight term associated with analysis result in the data memory module；Data analysis module, at least one physiological data of the data analysis module from data memory module needed for extraction and analysis result, and extract corresponding physiological data weight term, physiological data with physiological data weight term match and generates matching pair, to matching to being analyzed to obtain analysis result.
Description
Technical field
The present invention relates to health control analysis field, more particularly to the analysis system and method for a kind of physiological data.
Background technology
With the continuous development of medical technology and the increasingly raising of living standard, health control of the people for itself is also got over
More to pay attention to, treatment and control to existing disease are not only embodied in, is also had for sub-health state and high-risk disease occurred frequently
Early warning and early intervention, there is an urgent need to have a set of efficiently feasible analysis method and system to help people timely to understand itself
Health status to deploy prophylactico-therapeutic measures early.
Under currently existing technology, a part of inventor utilizes the high efficiency and accuracy of electronic message unit, to physiology number
According to being acquired, and data are analyzed, obtain explanation and make feedback, such as patent CN201310526557.4, describe one
The method that the physiological health achievement data of kind human body carries out Macro or mass analysis early warning, this method pass through every basic physiology to user
Data acquisition, record, analysis and early warning is carried out to exceeded data, and carry out message push.But its operation performed is only for list
The data analysis of item physical signs such as height, body weight, blood fat, blood glucose, blood pressure, waistline, fat, HRV indexs, and provide relative
The exceeded early warning answered, and the domestic consumer for lacking enough medical science attainment be then not easy to directly to understand it is specific shown in its early warning
Medical information, user also needs to further to go to look for instruction of take action accordingly in the information in push, and push provides
News depend only on the judgement of warning data classification and number, do not associate specific measurement index, therefore the information is right in itself
Lack more preferable specific aim for individual consumer.
And for example patent CN201280067587.7, describe explanation and physiological status management for improved physiological data
The system of the presentation of information, it is by measuring one or more physiological data parameters, by indicating and selecting user to be advertised to
Physiological patterns or data point explain data, and export selected information and it is optional recommend and/or action, with
Comprehensible and user-friendly mode, to be advantageous to managing physiologic situation.The program puts forth effort on user-interaction experience, more intuitively
Explanation physiological data and tack, reduce cognitive load, but although can use multiple different physiological datas measurement
As a result multiple corresponding physiological status are managed and carry out synchronism output, but for some by a variety of physiological data joint effects
The processing of physiological status, lack targetedly coping mechanism.
In addition, we have understood now, the physiological status of a people be by the combined effect of multinomial physiologic factor, and this
Do not only have the influence of congenital gene wherein, also have the intervention of the habits and customs day after tomorrow, this is also why we usually hear some
Centenarian have seemingly unfavorable healthy life habit but remain able to longevity (as custom drinks the unboiled water do not boiled or drinks)
Story, very simple the reason for its behind, individual event factor e insufficient to produce enough physiological effects sometimes.
Above prior art is by reaching " a pair to current physiological condition to the monitoring of existing physical signs
One " control, and how to judge to cooperate with influence to physiological status with a variety of physiologic factors such as analysis habits and customs, living environments
It is difficult to embody in the above-described techniques, meanwhile, the correlation of some physiologic factors and corresponding physiological status also can not be in prior art
In handled, therefore be also difficult to provide the physiological status of user more comprehensively accurate analysis.
Simultaneously, because the factor that may influence an individual risk is a lot, therefore number of combinations also can be very big.Physiology
No matter factor is divided into several classes (congenital, habits and customs, motion, diet, psychology), in what manner (physical examination report, questionnaire, individual
Medical history taking) collect, it is assumed that its quantum hypothesis be N, then its combination number of species be：C(N,1)+C(N,2)+C(N,3)
+ ...+C (N, N-1)+C (N, N), by taking N=6 as an example, this formula be C (N, 1)+C (N, 2)+C (N, 3)+C (N, 4)+C (N,
5)+C (N, 6)=41, as N=100, this numerical value will be very huge, because C therein (100,8) one reaches 186,
087,894,300 kind of possibility, and system physiologic factor accumulative in the future may be more than 100 kinds in reality, so if will be with
Common method finds out blocking factor, and its operation efficiency can be very low.
Therefore, against the background of the prior art, it is necessary to one kind can carry out scientific and efficient analysis to multinomial physiological data, and carry
For reliable prospective analysis, one of analysis and early warning in particular for the physiological status by a variety of physiological data joint effects
The analysis system and method for kind physiological data.
The content of the invention
The technical problems to be solved by the invention are overcome the deficiencies in the prior art, there is provided multinomial physiological data can be entered
The scientific and efficient analysis of row, and reliable prospective analysis is provided, in particular for the physiology by a variety of physiological data joint effects
The analysis system and method for a kind of physiological data of analysis and the early warning of situation.
In order to solve the above technical problems, the present invention provides a kind of analysis system and method for physiological data, it is characterised in that
The analysis system of the physiological data includes：Data acquisition module, data memory module, data analysis module, data output mould
Block；
Data acquisition module, the data acquisition module are used to gather physiological data, and the physiological data includes personal strong
The data sources related to physiologic information event such as health archives, physical examination report, questionnaire information, statistical information, the personal health shelves
Case includes audit report, the medical informations such as record of seeing a doctor；
Data memory module, the data memory module store to physiological data, are deposited in the data memory module
There is each physiological data weight term associated with analysis result；
Data analysis module, the data analysis module from data memory module needed for extraction and analysis result at least one
Item physiological data, and corresponding physiological data weight term is extracted, physiological data is matched with physiological data weight term
Generation matching pair, to matching to being analyzed to obtain analysis result；
Data outputting module, the data outputting module obtain analysis result and output it at data analysis module；
Data preprocessing module is provided with the data acquisition module, the data preprocessing module is carried out to physiological data
Structuring is handled, and the physiological data that the data memory module is handled structuring stores, and the data memory module can
To be local data base or cloud database, data traversal module, the data traversal mould are provided with the data analysis module
Block ergodic data memory module, extracts physiological data and corresponding physiological data weight term carries out matching generation matching pair,
The data analysis is refined to obtain normalized parameter A1, A2, A3, A4 corresponding with data source to analysis result, described to return
One change parameter is subtracted each other with system a reference value A1 ', A2 ', A3 ', A4 ', B1, B2, B3, B4 squares of gained difference is handled with take on the occasion of
B1 ', B2 ', B3 ', B4 ', i.e. B1 '=(A1-A1 ')2, B2 '=(A2-A2 ')2, B3 '=(A3-A3 ')2, B4 '=(A4-A4 ')2,
Health index A is provided to each individual, physical examination report weight accounts for 50%, and other each data source weights account for 50%；Then A=0.3*A1
+ 0.5*A2+0.1*A3+0.1*A4, health index A and system a reference value A ' are subtracted each other, to gained difference, B squares is handled to take just
Value B ', i.e. B '=(A-A ')2；
Data statistics module is additionally provided with the data memory module, the data statistics module is used for collection analysis result
And outside statistical information, and preserved using JSON character strings to what data combined, the data analysis module is united to data
JSON character strings in meter module are traveled through and parsed, for correcting physiological data weight term and being stored in data storage module；
Artificial intelligence module and big data analysis module, the artificial intelligence module are additionally provided with the data analysis module
Analysis process in data analysis module and analysis logic are learnt and optimized, the big data analysis module is tied to analysis
Fruit and outside statistical information are arranged and analyzed；
A kind of analysis method of physiological data, it is characterised in that the analysis method of the physiological data includes：Data are adopted
Collection, data storage, data analysis, data output；
Data acquisition, gather physiological data, the physiological data include personal health archives, physical examination report, questionnaire information,
The data source related to physiologic information event such as statistical information, the personal health archives include audit report, record etc. of seeing a doctor
Medical information；
Data storage, physiological data is stored；
Data analysis, at least one physiological data needed for extraction and analysis result, and extract corresponding physiological data
Weight term, physiological data and physiological data weight term are carried out to match generation matching pair, to matching to being analyzed
As a result；
Data output, the analysis result that data analysis is obtained export；
Data prediction is provided with the data acquisition, the data prediction is that physiological data is carried out at structuring
Reason, the data storage are that the physiological data of structuring processing is stored, and the data storage can be with locally stored number
According to storehouse or cloud database, data traversal is provided with the data analysis, the data traversal is to store mould by ergodic data
Block, extracts physiological data and corresponding physiological data weight term carries out matching generation matching pair, and the data analysis is to dividing
Analysis result is refined to obtain normalized parameter A1, A2, A3, A4 corresponding with data source, the normalized parameter and system base
Quasi- value A1 ', A2 ', A3 ', A4 ' subtract each other, to B1, B2, B3, B4 squares of processing of gained difference to take on the occasion of B1 ', B2 ', B3 ', B4 ',
That is B1 '=(A1-A1 ')2, B2 '=(A2-A2 ')2, B3 '=(A3-A3 ')2, B4 '=(A4-A4 ')2, each individual is provided strong
Health Index A, physical examination report weight account for 50%, and other each data source weights account for 50%；Then
A=0.3*A1+0.5*A2+0.1*A3+0.1*A4, health index A and system a reference value A ' are subtracted each other, it is poor to gained
B squares of value is handled to take on the occasion of B ', i.e. B '=(A-A ')2；
Data statistics is additionally provided with the data storage, the data statistics is for collection analysis result and outside statistics
Information, and preserved using JSON character strings to what data combined, the data analysis is to analysis result and outside statistics letter
JSON character strings in breath are traveled through and parsed, for correcting physiological data weight term and being stored in data storage module；
The data analysis is additionally provided with artificial intelligence and big data analysis, and the artificial intelligence is to point in data analysis
Analysis flow and analysis logic are learnt and optimized, and the big data analysis is whole to analysis result and the progress of outside statistical information
Reason and analysis.
The beneficial effect that the present invention is reached:
Scientific and efficient analysis can be carried out to multinomial physiological data, and reliable prospective analysis, especially pin are provided
Analysis and early warning to the physiological status by a variety of physiological data joint effects.
Brief description of the drawings
Fig. 1 is the system structure diagram of the exemplary embodiment of the present invention；
Fig. 2 is the structuring processing and analysis process schematic diagram that the exemplary embodiment of the present invention is reported physical examination；
Fig. 3 is structuring processing and analysis process schematic diagram of the exemplary embodiment of the present invention to questionnaire information；
Fig. 4 is the content citing of questionnaire information in exemplary embodiment of the invention；
Fig. 5 is structuring processing and analysis process schematic diagram of the exemplary embodiment of the present invention to JSON character strings；
Fig. 6 is the analysis process signal that the exemplary embodiment of the present invention is modified to the weight term of JSON character strings
Figure；
Fig. 7 be the present invention exemplary embodiment in JSON character string informations and questionnaire information content associate citing；
Fig. 8 is analysis process of the exemplary embodiment to health index of the present invention.
Embodiment
The present invention is further illustrated with exemplary embodiment below in conjunction with the accompanying drawings：
As shown in figure 1, the present invention is by data acquisition module 1, data memory module 2, data analysis module 3 and data output
Module 4 is formed, and data preprocessing module 5 is provided with wherein in data acquisition module, data statistics mould is provided with data memory module
Block 7, data traversal module 6, big data analysis module and artificial intelligence module are provided with data analysis module.
Data acquisition module is used to gather physiological data, and physiological data includes personal health archives, physical examination report, questionnaire letter
The data source related to physiologic information event such as breath, statistical information, personal health archives include audit report, record etc. of seeing a doctor is examined
Information is treated, data preprocessing module carries out structuring processing to physiological data, and is deposited into data memory module, data storage
Module can be local data base or cloud database, also have each life associated with analysis result in data memory module
Data weighting item is managed, includes (but not limited to) such as expert graded, investigation statisticses method, analytic hierarchy process (AHP) in physiological data weight term
Different specific weight models.
The requirement of matching pair of the data analysis module according to needed for analysis result, with data traversal module from data storage mould
At least one physiological data needed for analysis result is traveled through in block, and travels through corresponding physiological data weight term, by physiology
Data carry out matching generation matching pair with physiological data weight term, then to matching to being analyzed to obtain analysis result, finally
Analysis result is exported by data outputting module, as shown in figure 8, data analysis module will also refine to analysis result
Obtain normalized parameter A1, A2, A3, A4 corresponding with data source, by the normalized parameter and system a reference value A1 ', A2 ',
A3 ', A4 ' subtract each other, and to gained difference, B1, B2, B3, B4 squares are handled to take on the occasion of B1 ', B2 ', B3 ', B4 ', i.e. B1 '=(A1-
A1’)2, B2 '=(A2-A2 ')2, B3 '=(A3-A3 ')2, B4 '=(A4-A4 ')2, the difference B1, B2, B3, B4 reflection analysis knot
Fruit compared to standard state positive and negative correlation circumstance, should on the occasion of B1 ', B2 ', B3 ', B4 ' then reflect and the analysis result compared to
The extent of deviation of standard state, so as to reflect the deviation situation with health status.Further, health is provided to each individual
Index A, physical examination report weight account for 50%, and other each data source weights account for 50%；Then
A=0.3*A1+0.5*A2+0.1*A3+0.1*A4, health index A and system a reference value A ' are subtracted each other, it is poor to gained
B squares of value is handled to take on the occasion of B ', i.e. B '=(A-A ')2, then the health status change by different data sources combined influence can be obtained
Change situation.
In addition, the data statistics module in data memory module is used to gather previous analysis result and outside statistics letter
Breath, and preserved using JSON character strings to what various data groups therein were closed, as shown in figure 5, data analysis module is same
JSON character strings and physiological event can be traveled through, obtain matching pair and analyzed accordingly, the analysis result can use
In amendment physiological data weight term and it is stored in data storage module.
For example, it is the true preservation form of one about 27 topic questionnaire in systems below, is with JSON character strings
Mode save option selected by the ID and user of each questionnaire problem.Except JSON character strings can also be used in practice
Other modes preserve, such as serializing character string or the character string of user-defined format, as long as total system is unified, facilitate character string
Match somebody with somebody.If preserving these data with general database form, the questionnaire of 27 problems then needs 27 rows to preserve, with character
String form then only needs a field in a line to preserve：
[{"id":"9","ans":""},{"id":"10","ans":""},{"id":"13","ans":""},{"
id":"14","ans":""},{"id":"64","ans":"2,"},{"id":"65","ans":"2,"},{"id":"66","
ans":"1,"},{"id":"67","ans":"1,"},{"id":"68","ans":"1,2,4,"},{"id":"69","
ans":"2,"},{"id":"76","ans":"2,"},{"id":"22","ans":"1,"},{"id":"23","ans":"
1,"},{"id":"24","ans":"4,"},{"id":"25","ans":"2,"},{"id":"26","ans":"1,"},{"
id":"27","ans":"1,"},{"id":"28","ans":"1,"},{"id":"29","ans":"1,"},{"id":"
30","ans":"1,"},{"id":"31","ans":"1,"},{"id":"70","ans":"3,"},{"id":"72","
ans":"3,"},{"id":"73","ans":"1,"},{"id":"74","ans":"2,"},{"id":"75","ans":"
4,"},{"id":"71","ans":"2,"}]
By taking the final stage in above-mentioned character string as an example, it is exactly " for the questionnaire problem that ID is 71, the user to parse
Answer be selection 2 ", carry out information such as Fig. 7 by the questionnaire expansion that data base querying ID is 71, i.e. the daily vegetables of the user take the photograph
It is 100g~300g to enter amount.
As shown in fig. 6, when increasing physiological event (such as case) newly in a system and occurring, i.e., by its physiological data or
When the analysis of analysis result can directly regard as physiological event (such as case), we can just obtain the source of the physiological event,
That is respective data sources, and the JSON character strings in related data sources are parsed to obtain its specific physiological data combination, then with being
The existing physiological data combination preserved in system carries out the matching analysis, to decide whether to finely tune the weighted value each combined.
Such as：Assuming that new cases are lung cancer, network analysis its cause the physiological data of lung cancer to come from following three differences
The physiological data combination of data source：
Combination 1：
{"id":"67","ans":"1,"},{"id":"68","ans":"1,2,4,"},{"id":"69","ans":"
2,"},{"id":"76","ans":"2,"},{"id":"22","ans":"1,"}]
Combination 2：
[{"id":"23","ans":"1,"},{"id":"24","ans":"4,"},{"id":"25","ans":"
2,"}]
Combination 3：
[{"id":"26","ans":"1,"},{"id":"27","ans":"1,"},{"id":"28","ans":"
1,"},{"id":"29","ans":"1,"},{"id":"30","ans":"1,"},{"id":"31","ans":"1,"},{"
id":"70","ans":"3,"},{"id":"72","ans":"3,"}]
Then three kinds of physiological datas combine base of the lung cancer associated weight item needs in currency of included physiological data
On plinth plus X, X depend on the accumulation and analysis of data in system.
Accordingly, in system increases case newly, the physiological data included in the related physiological data combination of healthy individuals
It would potentially result in the reduction of its some physiological data weight term.With being continuously increased for sample size, it is right that data analysis module passes through
The continuous analysis of data statistics module, it will constantly leveled off in the adjustment of physiological data weight term scientific and reasonable.
As shown in Fig. 2 physical examination report is handled by structuring, detection one by one is decomposed into, data analysis module leads to
The traversal to detection and physiological data weight term (i.e. the associated weight item of detection) is crossed, searches out matching pair, then to matching
To being analyzed so as to obtaining required analysis result.
As shown in Figure 3, Figure 4, questionnaire information also passes through structuring processing, resolves into problem one by one and corresponding answers
Case, data analysis module by the traversal to question and answer content and physiological data weight term (i.e. the associated weight item of question and answer content),
Matching pair is searched out, then to matching to being analyzed so as to obtaining required analysis result.This part and above-mentioned physical examination report
Analysis process is compared, and its difference is, physical examination report contains more digital informations about physical signs, such as body
Height, body weight, age, blood pressure, blood glucose, heart rate etc., and questionnaire information contains more relevant habits and customs or psychological condition
The combination of text information or data and word, such as smoking history, sport history, sleep state, mental health state.
In addition, the big data analysis module in data analysis module further can be entered to the data in data memory module
Row arranges and analysis, and the analysis result can be used for screening more scientific and reasonable physiological data combination；As carried in background technology,
The addition of more physiologic factors can make analysis result more scientific and reasonable to a certain extent, but the number of combinations mistake of physiologic factor
In huge, the operational efficiency of system can be also negatively affected, therefore by the processing of big data analysis module, there can be pin
Property is adjusted to physiological data combination, retains or increases the high physiological data of physiological data weight term, cut down physiology number
According to the low physiological data of weight term, so that the storage pressure for efficiently, reducing system is more simplified in the physiological data combination in system
Power and computing pressure, improve the operational efficiency and accuracy of system.And artificial intelligence module is then to point in data analysis module
Analysis flow and analysis logic are learnt and optimized, and pass through the experience accumulation of system, assistance data spider module optimization ergodic flow
Journey, improve the hunting speed and accuracy of matching pair, assistance data analysis module adjustment physiological data weight term, normalized parameter
System a reference value and each data source weight, make analysis result it is more scientific rationally, auxiliary big data analysis module to each physiology
Data combination is improved, and makes the operating efficiency of whole system higher.
Present invention is mainly used for scientific and efficient analysis, and reliably perspective point of offer are carried out to multinomial physiological data
Analysis, in particular for the analysis and prediction of the physiological status by a variety of physiological data joint effects.
Above example does not limit the present invention in any way, every that above example made in a manner of equivalent transformation
Other improvement and application, belong to protection scope of the present invention.
Claims (8)
1. a kind of analysis system of physiological data, it is characterised in that the analysis system of the physiological data includes：Data acquisition module
Block, data memory module, data analysis module, data outputting module；
Data acquisition module, the data acquisition module are used to gather physiological data, and the physiological data is derived from personal health shelves
The data sources related to physiologic information event such as case, physical examination report, questionnaire information, statistical information, the personal health archives bag
Include audit report, the medical informations such as record of seeing a doctor；
Data memory module, the data memory module store to physiological data, have in the data memory module with
The associated each physiological data weight term of analysis result；
Data analysis module, at least one of the data analysis module from data memory module needed for extraction and analysis result are raw
Data are managed, and extract corresponding physiological data weight term, physiological data and physiological data weight term are subjected to matching generation
Matching pair, to matching to being analyzed to obtain analysis result；
Data outputting module, the data outputting module obtain analysis result and output it at data analysis module.
2. a kind of analysis system of physiological data as claimed in claim 1, it is characterised in that set in the data acquisition module
There is data preprocessing module, the data preprocessing module carries out structuring processing, the data memory module to physiological data
The physiological data of structuring processing is stored, the data memory module can be local data base or cloud database,
Data traversal module is provided with the data analysis module, the data traversal module walks data memory module, extracts physiology
Data and corresponding physiological data weight term carry out matching generation matching pair；The data analysis module enters to analysis result
Row refinement obtains normalized parameter A1, A2, A3, A4 corresponding with data source, the normalized parameter and system a reference value A1 ',
A2 ', A3 ', A4 ' subtract each other, to the processing of B1, B2, B3, B4 squares of gained difference to take on the occasion of B1 ', B2 ', B3 ', B4 ', i.e. B1 '=
(A1-A1’)2, B2 '=(A2-A2 ')2, B3 '=(A3-A3 ')2, B4 '=(A4-A4 ')2, health index is provided to each individual
A, physical examination report weight account for 50%, and other each data source weights account for 50%, then
A=0.3*A1+0.5*A2+0.1*A3+0.1*A4, health index A and system a reference value A ' are subtracted each other, to gained difference B
Square processing is to take on the occasion of B ', i.e. B '=(A-A ')2。
3. a kind of analysis system of physiological data as claimed in claim 2, it is characterised in that in the data memory module also
Provided with data statistics module, the data statistics module is used for collection analysis result and outside statistical information, and uses JSON words
Symbol string preserving to data combination, the data analysis module travels through to the JSON character strings in data statistics module
And parse, for correcting physiological data weight term and being stored in data storage module.
4. a kind of analysis system of physiological data as claimed in claim 3, it is characterised in that in the data analysis module also
Provided with artificial intelligence module and big data analysis module, the artificial intelligence module to the analysis process in data analysis module and
Analysis logic is learnt and optimized, and the big data analysis module is arranged and divided to analysis result and outside statistical information
Analysis.
5. a kind of analysis method of physiological data, it is characterised in that the analysis method of the physiological data includes：Data acquisition,
Data storage, data analysis, data output；
Data acquisition, gathers physiological data, and the physiological data includes personal health archives, physical examination report, questionnaire information, statistics
The data source related to physiologic information event such as information, the personal health archives include audit report, the diagnosis and treatment such as record of seeing a doctor
Information；
Data storage, physiological data is stored；
Data analysis, at least one physiological data needed for extraction and analysis result, and extract corresponding physiological data weight
, physiological data and physiological data weight term match generation and matched pair, to matching to being analyzed to obtain analysis result；
Data output, the analysis result that data analysis is obtained export.
6. a kind of analysis method of physiological data as claimed in claim 5, it is characterised in that number is provided with the data acquisition
Data preprocess, the data prediction are that structuring processing is carried out to physiological data, and the data storage is that structuring is handled
Physiological data stored, the data storage can be with locally stored database or cloud database, the data analysis
In be provided with data traversal, the data traversal is by ergodic data memory module, extracts physiological data and corresponding life
Reason data weighting item carries out matching generation matching pair, and the data analysis is refined to obtain corresponding to data source to analysis result
Normalized parameter A1, A2, A3, A4, the normalized parameter subtracts each other with system a reference value A1 ', A2 ', A3 ', A4 ', to gained
B1, B2, B3, B4 squares of difference is handled to take on the occasion of B1 ', B2 ', B3 ', B4 ', i.e. B1 '=(A1-A1 ')2, B2 '=(A2-A2 ')2, B3 '=(A3-A3 ')2, B4 '=(A4-A4 ')2, health index A is provided to each individual, physical examination report weight accounts for 50%, its
Its each data source weight accounts for 50%；Then A=0.3*A1+0.5*A2+0.1*A3+0.1*A4, by health index A and system a reference value
A ' subtracts each other, and to gained difference, B squares is handled to take on the occasion of B ', i.e. B '=(A-A ')2。
7. a kind of analysis method of physiological data as claimed in claim 6, it is characterised in that be additionally provided with the data storage
Data statistics, the data statistics are to be used for collection analysis result and outside statistical information, and using JSON character strings to data
Combination preserve, and the data analysis is traveled through and solved to the JSON character strings in analysis result and outside statistical information
Analysis, for correcting physiological data weight term and being stored in data storage module.
8. a kind of analysis method of physiological data as claimed in claim 7, it is characterised in that the data analysis is additionally provided with people
Work intelligence and big data analysis, the artificial intelligence are that the analysis process in data analysis and analysis logic are learnt and excellent
Change, the big data analysis is that analysis result and outside statistical information are arranged and analyzed.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109886818A (en) * | 2019-01-14 | 2019-06-14 | 成都数保科技有限公司 | A kind of accurate analysis system of medical insurance business datum based on big data |
| CN111584000A (en) * | 2020-05-07 | 2020-08-25 | 重庆医科大学 | Behavior intervention method based on network fusion gene information feedback |
| WO2020258483A1 (en) * | 2019-06-27 | 2020-12-30 | 齐鲁工业大学 | Clinical medication behavior analysis system based on highly effective negative sequential mining pattern, and working method therefor |
| WO2021120078A1 (en) * | 2019-12-19 | 2021-06-24 | 杭州星迈科技有限公司 | Seizure early-warning method and system |
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