CN105653859A - Medical big data based disease automatic assistance diagnosis system and method - Google Patents

Medical big data based disease automatic assistance diagnosis system and method Download PDF

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Publication number
CN105653859A
CN105653859A CN201511022291.5A CN201511022291A CN105653859A CN 105653859 A CN105653859 A CN 105653859A CN 201511022291 A CN201511022291 A CN 201511022291A CN 105653859 A CN105653859 A CN 105653859A
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disease
symptom
diagnosis
definite diagnosis
sympotomatic
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罗旭
杨君
常永虎
何欢
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Zunyi Medical University
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Zunyi Medical University
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

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Abstract

The present invention discloses a medical big data based disease automatic assistance diagnosis system and method. The system comprises: a background data storage unit; an information processing unit, which specifically comprises: a statistical classification module, used for acquiring case data in the background data storage unit, and performing statistical classification on the case data, so as to obtain a symptom set and a definitively diagnosed disease type set; a diagnosed disease set calculation module, used for calculating a definitively diagnosed symptom set of various diseases according to the symptom set and the disease type set that are obtained by the statistical classification module; and a disease automatic diagnosis module, used for acquiring disease symptom data provided by a user, generating a selection symptom set, comparing the selection symptom set with the definitively diagnosed symptom set of various diseases and performing calculation, so as to obtain a disease determination result; and a man-machine interaction unit, used for displaying an interface of selecting a disease by the user, and outputting a disease diagnosis result. The method disclosed by the present invention is simple, easy and strong in operatability, and provides a new clinic assistant diagnosis tool for the medical field, and reduces an error/miss diagnosis rate.

Description

A kind of automatic assistant diagnosis system of disease based on the big data of medical treatment and method
Technical field
The present invention relates to medical information to calculate and data processing field, particularly relate to a kind of automatic assistant diagnosis system of disease based on the big data of medical treatment and method.
Background technology
Sufferer individual variation is big, and medical condition is of a great variety, and combined condition is common and relation is complicated, needs that many doctors are multidisciplinary to work in coordination with together during diagnosis, and diagnosis is difficult to standardization and automatization so that originally nervous medical resource is had too many difficulties to cope with especially; And medical procedure lacks being actively engaged in of patient, the working strength adding doctor is big, there is many Misdiagnosis phenomenons, the problem causing physician-patient relationship tense. Although the healthy net of a lot of online hospitals such as 39 can alleviate a part of pressure, but owing to lacking detailed diagnosis data, the diagnosis provided often is lost biased, and is no lack of malicious person to the diagnosis made mistake.
Along with the progress of the Internet and medical skill, the solution that big data analysis is many difficult medical problem provides new way. And existing mobile Internet medical terminal function is comparatively single, only there is the function of data acquisition transmission, without the function of intelligence medical diagnosis on disease.
Therefore, lack the automatic assistant diagnosis system of disease of a kind of intelligence at present, using the teaching of the invention it is possible to provide the reference result of medical diagnosis on disease, be used for alleviating doctor consuming time in medical diagnosis on disease process, inefficient problem.
Summary of the invention
The technical problem to be solved in the present invention is in that to diagnose the illness length consuming time for doctor in prior art, inefficient defect, it is provided that a kind of automatic assistant diagnosis system of disease based on the big data of medical treatment that can quickly automatically diagnose the illness and method.
The technical solution adopted for the present invention to solve the technical problems is:
The present invention provides a kind of automatic assistant diagnosis system of disease based on the big data of medical treatment, including:
Back-end data memory element, storage has a large amount of existing case data;
Information process unit, specifically includes statistical classification module, makes a definite diagnosis sympotomatic set and ask for module and the automatic diagnostic module of disease, wherein:
Statistical classification module, for obtaining the case data in back-end data memory element, and carries out statistical classification to it, the disease type collection obtaining sympotomatic set with making a definite diagnosis;
Making a definite diagnosis sympotomatic set and ask for module, for the sympotomatic set obtained according to statistical classification module and disease type collection, that asks for various disease makes a definite diagnosis sympotomatic set;
The automatic diagnostic module of disease, for obtaining the disease symptoms data that user provides, generates and selects sympotomatic set, it is compared with the sympotomatic set of making a definite diagnosis of various diseases and calculated, obtains Diseases diagnosis result;
Man-machine interaction unit, for showing that user selects the interface of symptom, and exports the diagnostic result of disease.
The present invention provides a kind of automatic aided diagnosis method of disease based on the big data of medical treatment, comprises the following steps:
S1, statistical classification module obtain the case data in back-end data memory element, and it is carried out the disease type collection that statistical classification obtains sympotomatic set and makes a definite diagnosis, and by make a definite diagnosis sympotomatic set ask for module calculate further obtain various disease make a definite diagnosis sympotomatic set;
S2, user select position and the symptom of disease by man-machine interaction unit, and the data genaration that information process unit provides according to user selects sympotomatic set;
The automatic diagnostic module of S3, disease compares according to selecting sympotomatic set and the sympotomatic set of making a definite diagnosis of each sick type being stored in background data base, calculate and select sympotomatic set and make a definite diagnosis the difference of symptom number in sympotomatic set, with each difference of obtaining for basis for estimation, it is ranked up ascending for difference;
The result of S4, output diagnosis automatically, lists the relevant of first three items difference and makes a definite diagnosis sick type corresponding to sympotomatic set and referential.
Further, in the step S1 of the present invention, sympotomatic set is made a definite diagnosis in acquisition method particularly includes:
Obvious relation between persistence is had, when symptom A often occurs in the case that diagnosis is disease B, it is judged that symptom A is the dominating symptom of disease B between symptom A and disease B; What all dominating symptom of disease B constituted this disease makes a definite diagnosis sympotomatic set.
Further, the step S1 of the present invention ask for make a definite diagnosis sympotomatic set method particularly includes:
Step a: given threshold value beta, �� >=0.6, symptom sum I, sick type sum J, make count value i=1, j=1;
Step b: statistics symptom ZiWith disease BjBetween dependency C (Zi,Bj), namely it is diagnosed as disease type BjContaining symptom ZiProbability;
Step c: as symptom ZiWith BjBetween disease, dependency is more than threshold value beta, then symptom Z is describediIt is disease BjDominating symptom, by ZiSymptom brings disease B intojMake a definite diagnosis in sympotomatic set, put i=i+1, when i <=I then goes to step b, when i > I then skips to step d;
Step d: synthetic disease BjAll of dominating symptom, the set of composition is disease BjMake a definite diagnosis sympotomatic set, put j=j+1, i=1, as j > J, the process of asking for making a definite diagnosis sympotomatic set terminates, otherwise then forwards step b to.
Further, the step S3 of the present invention calculates difference method particularly includes:
The symptom that user provides is sent to the automatic diagnostic module of disease by man machine interface, and the retrieval of this module is made a definite diagnosis sympotomatic set by what make a definite diagnosis that sympotomatic set asks for various diseases that module provides, by all disease Bj, j=1,2,3 ..., the difference of the symptom number that the symptom number made a definite diagnosis in sympotomatic set of J and user provide namely | Y (Q (Bj))-Y (G (Z)) | by sorting from small to large;
Wherein, disease BjSympotomatic set of making a definite diagnosis be Q (Bj), the symptom number in this set is Y (Q (Bj)), the set that the symptom that user provides is constituted is G (Z), and the symptom number in this set is Y (G (Z)), | Y (Q (Bj))-Y (G (Z)) | represent the difference of symptom number in two set.
Further, the informative computing formula that in the step S4 of the present invention, the sick type of diagnosis is corresponding is:
1-|Y(Q(Bj))-Y(G(Z))|/max(Y(G(Z)),Y(Q(Bj)))
Wherein, disease BjSympotomatic set of making a definite diagnosis be Q (Bj), the symptom number in this set is Y (Q (Bj));The set that the symptom that patient provides is constituted is G (Z), and the symptom number in this set is Y (G (Z)), max (Y (G (Z)), Y (Q (Bj))) represent take Y (Q (Bj)) and the Y (G (Z)) maximum in both.
The beneficial effect comprise that: the automatic assistant diagnosis system of disease based on the big data of medical treatment of the present invention and method, by obtaining the relation between disease symptoms and diagnosed disease, and the data automatic decision patient according to patient's offer is suffered from the disease, quickly definitely diagnose the state of an illness for patient and doctor and objective reference result is provided; The present invention is simple and easy to do, can moral conduct strong, and owing to giving the man-machine interactive system carrying out symptom selection by sick puberty body region, patient has been encouraged to be actively engaged in medical treatment, a kind of new clinical assistant diagnosis instrument is provided for medical circle, assist physician is quickly investigated and is diagnosed patient and suffered from the disease, and reduces mistake/rate of missed diagnosis; The present invention also alleviates the problem that medical resource is limited, and then alleviates the conflict between doctors and patients problem that presently, there are; During hardware is implemented, it is also separable that this facility information processor and display module can be integrated machine, and when disengaged, display module can remotely and message processing module communicating data.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described, in accompanying drawing:
Fig. 1 is the structural representation of the automatic assistant diagnosis system of disease based on the big data of medical treatment of the embodiment of the present invention;
Fig. 2 is the workflow diagram of the automatic aided diagnosis method of disease based on the big data of medical treatment of the embodiment of the present invention;
Fig. 3 is the flow chart of the automatic aided diagnosis method of disease based on the big data of medical treatment of the embodiment of the present invention;
Fig. 4 is the workflow diagram of the automatic diagnostic module of the automatic assistant diagnosis system of disease based on the big data of medical treatment of the embodiment of the present invention;
Fig. 5 is the interface (1) selecting position of disease of the automatic assistant diagnosis system of disease based on the big data of medical treatment of the embodiment of the present invention;
Fig. 6 is the interface (2) selecting position of disease of the automatic assistant diagnosis system of disease based on the big data of medical treatment of the embodiment of the present invention;
Fig. 7 is the interface that the judgement based on the automatic assistant diagnosis system of disease of the big data of medical treatment of the embodiment of the present invention suffers from the disease.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated. Should be appreciated that specific embodiment described herein is only in order to explain the present invention, is not intended to limit the present invention.
As it is shown in figure 1, the automatic assistant diagnosis system of disease based on the big data of medical treatment of the embodiment of the present invention, including:
Back-end data memory element, storage has a large amount of existing case data;
Information process unit, specifically includes statistical classification module, makes a definite diagnosis sympotomatic set and ask for module and the automatic diagnostic module of disease, wherein:
Statistical classification module, for obtaining the case data in back-end data memory element, and carries out statistical classification to it, and the disease type collection obtaining sympotomatic set with making a definite diagnosis summarizes pathological symptom and disease;
Making a definite diagnosis sympotomatic set and ask for module, for based on the case notes data in background data base, the sympotomatic set obtained according to statistical classification module and disease type collection, that asks for various disease makes a definite diagnosis sympotomatic set;
The automatic diagnostic module of disease, for obtaining the disease symptoms data that user provides, generates and selects sympotomatic set, it is compared with the sympotomatic set of making a definite diagnosis of various diseases and calculated, obtains Diseases diagnosis result;
Man-machine interaction unit, for showing that user selects the interface of symptom, enumerates out all kinds of symptom and selects for user, and export the diagnostic result of disease.
The nominal definition related to during native system is worked and labelling are unitedly described as follows:
A) dominating symptom: have obvious relation between persistence between symptom A and disease B, when often there is symptom A in the case that diagnosis is disease B, then claiming symptom A is the dominating symptom of disease B; B) relatedness between symptom A and disease B: namely have the probability of symptom A in disease B, when the case having K item disease B in the data of typing, and these cases have symptom A for M item, then the dependency (probability) between A and B is M/K; C) sympotomatic set is made a definite diagnosis: what all dominating symptom of disease B constituted this disease makes a definite diagnosis sympotomatic set;
Note { Zi| i=1,2,3 ..., I} is sympotomatic set, altogether I kind symptom; Note { Bj| j=1,2,3 ..., J} is disease type collection, altogether J kind disease; Note C (Zi,Bj) represent ZiWith BjBetween relatedness; Note BjSympotomatic set of making a definite diagnosis be Q (Bj), the symptom number in set is Y (Q (Bj)); The set that the symptom that note patient provides is constituted is G (Z), and the symptom number in this set is Y (G (Z)). Max (Y (G (Z)), Y (Q (Bj))) represent take Y (Q (Bj)) and the Y (G (Z)) maximum in both.
As shown in Figure 2, the embodiment of the present invention based on the automatic assistant diagnosis system of disease of the big data of medical treatment and the workflow of method be: the symptom that user provides is sent to the automatic diagnostic module of disease by man machine interface, ask for each disease that module provides and make a definite diagnosis sympotomatic set by making a definite diagnosis sympotomatic set again, the symptom that user provides is sent to " the automatic diagnostic module of disease " by man machine interface, each disease that the retrieval of this module is provided by " make a definite diagnosis sympotomatic set and ask for module " makes a definite diagnosis sympotomatic set, by all disease Bj, j=1,2,3 ..., the difference of the symptom number that the symptom number made a definite diagnosis in sympotomatic set of J and user provide namely | Y (Q (Bj))-Y (G (Z)) | by sorting from small to large. List disease corresponding to first three items and with reference to feasibility (i.e. 1-| Y (Q (Bj))-Y(G(Z))|/max(Y(G(Z)),Y(Q(Bj))) value), machine interface display of making a gift to someone. If disease corresponding for all differences being classified as less than three feasibility diagnosis, machine interface display that correspondence disease and reference feasibility are made a gift to someone.
As it is shown on figure 3, the automatic aided diagnosis method of disease based on the big data of medical treatment of the embodiment of the present invention, comprise the following steps:
S1, statistical classification module obtain the case data in back-end data memory element, and it is carried out the disease type collection that statistical classification obtains sympotomatic set and makes a definite diagnosis, and by make a definite diagnosis sympotomatic set ask for module calculate further obtain various disease make a definite diagnosis sympotomatic set;
Obtain and make a definite diagnosis sympotomatic set method particularly includes:
Obvious relation between persistence is had, when symptom A often occurs in the case that diagnosis is disease B, it is judged that symptom A is the dominating symptom of disease B between symptom A and disease B; What all dominating symptom of disease B constituted this disease makes a definite diagnosis sympotomatic set.
As shown in Figure 4, ask for and make a definite diagnosis sympotomatic set method particularly includes:
Step a: given threshold value beta, �� >=0.6, symptom sum I, sick type sum J. Make count value i=1, j=1;
Step b: statistics symptom ZiWith disease BjBetween dependency C (Zi,Bj), namely it is diagnosed as disease type BjContaining symptom ZiProbability;
Step c: as symptom ZiWith BjBetween disease, dependency is more than threshold value beta, then symptom Z is describediIt is disease BjDominating symptom, by ZiSymptom brings disease B intojMake a definite diagnosis in sympotomatic set, put i=i+1, when i <=I then goes to step b, when i > I then skips to step d;
Step d: synthetic disease BjAll of dominating symptom, the set of composition is disease BjMake a definite diagnosis sympotomatic set, put j=j+1, i=1, as j > J, the process of asking for making a definite diagnosis sympotomatic set terminates, otherwise then forwards step b to.
S2, user select position and the symptom of disease by man-machine interaction unit, and the data genaration that information process unit provides according to user selects sympotomatic set;
The automatic diagnostic module of S3, disease compares according to selecting sympotomatic set and the sympotomatic set of making a definite diagnosis of each sick type being stored in background data base, calculate and select sympotomatic set and make a definite diagnosis the difference of symptom number in sympotomatic set, according to each difference obtained as basis for estimation, it is ranked up ascending for difference;
Calculate difference method particularly includes:
The symptom that user provides is sent to the automatic diagnostic module of disease by man machine interface, and the retrieval of this module is asked for each disease that module provides and made a definite diagnosis sympotomatic set by making a definite diagnosis sympotomatic set, by all disease Bj, j=1,2,3 ..., the difference of the symptom number that the symptom number made a definite diagnosis in sympotomatic set of J and user provide namely | Y (Q (Bj))-Y (G (Z)) | by sorting from small to large;
Wherein, disease BjSympotomatic set of making a definite diagnosis be Q (Bj), the symptom number in this set is Y (Q (Bj)), the set that the symptom that user provides is constituted is G (Z), and the symptom number in this set is Y (G (Z)), | Y (Q (Bj))-Y (G (Z)) | represent the difference of symptom number in two set.
The result of S4, output diagnosis automatically, including the referential of the sick type diagnosed and correspondence;
The informative computing formula that the sick type of diagnosis is corresponding is:
1-|Y(Q(Bj))-Y(G(Z))|/max(Y(G(Z)),Y(Q(Bj)))
Wherein, disease BjSympotomatic set of making a definite diagnosis be Q (Bj), the symptom number in set is Y (Q (Bj)); The set that the symptom that patient provides is constituted is G (Z), and the symptom number in this set is Y (G (Z)). Max (Y (G (Z)), Y (Q (Bj))) represent take Y (Q (Bj)) and the Y (G (Z)) maximum in both.
As shown in Fig. 5, Fig. 6, Fig. 7, it it is the operation chart of man machine interface in an alternative embodiment of the invention, for the embodiment only diagnosing double; two sick type " flu " (acute nasopharyngitis) and " rhinitis ", first background context is introduced, then provide the information flag used, introduce successively how to obtain " flu " and " rhinitis " make a definite diagnosis sympotomatic set and how system diagnoses both diseases:
In the present embodiment, data deposit in large capacity disc array, and Database Systems and message processing module realize on x86 server, and display module realizes on long-range APP.
(1), in the case data in given data storehouse, by symptom, " statistical classification module " occurs that symptom is classified by position, sift out in advance and (be designated as B with catching a cold1) relevant symptom has: headache, dizziness, watery nasal discharge, nasal obstruction, heating, limbs fatigue, it is designated as respectively The frequency that these symptoms occur in the case be diagnosed as flu is: 40%, 70%, 90%, 90%, 70%, 60%, remember relatedness C ( Z 1 ( 1 ) , B 1 ) = 0.4 , C ( Z 1 ( 2 ) , B 1 ) = 0.7 , C ( Z 1 ( 3 ) , B 1 ) = 0.9 , C ( Z 1 ( 4 ) , B 1 ) = 0.9 , C ( Z 1 ( 5 ) , B 1 ) = 0.7 , C ( Z 1 ( 6 ) , B 1 ) = 0.6 ; Note and B1Relevant sympotomatic set is B 1 - { Z 1 ( k ) | k = 1 , 2 , ... , 6 } .
In case data in given data storehouse, sift out in advance and (be designated as B with rhinitis2) relevant symptom has: rhinalgia, watery nasal discharge, nasal obstruction, it is designated as respectivelyThe frequency that these symptoms occur in the case be diagnosed as flu is: 40%, 50%, 90%, remember relatedness C ( Z 1 ( 2 ) , B 1 ) = 0.5 , C ( Z 1 ( 3 ) , B 1 ) = 0.9 , Note and B2Relevant sympotomatic set is B 2 - { Z 2 ( k ) | k = 1 , 2 , 3 } .
(2) symptom that user selects is: dizziness, watery nasal discharge, nasal obstruction, heating.
Ask " making a definite diagnosis sympotomatic set ": given threshold value beta=0.6, due to C ( Z 1 ( 3 ) , B 1 ) > &beta; , C ( Z 1 ( 4 ) , B 1 ) > &beta; , C ( Z 1 ( 5 ) , B 1 ) > &beta; , C ( Z 1 ( 7 ) , B 1 ) > = &beta; , The sympotomatic set of making a definite diagnosis of " flu " isI.e. { dizziness, watery nasal discharge, nasal obstruction, heating, limbs fatigue }.Due to C ( Z 2 ( 1 ) , B 1 ) < &beta; , C ( Z 2 ( 2 ) , B 1 ) > &beta; , C ( Z 2 ( 3 ) , B 1 ) > &beta; , The sympotomatic set of making a definite diagnosis of " rhinitis " is B 2 - { Z 2 ( k ) | k = 2 , 3 } I.e. { watery nasal discharge, nasal obstruction }.
Make a definite diagnosis and suffered from the disease: the set that the symptom that known user provides is constituted for { dizziness, watery nasal discharge, nasal obstruction, heating }, itself andIn the difference of element number be 1, withIn the difference of element number be 2. Being concluded that diagnosis patient is suffered from the disease the feasibility for catch a cold (acute nasopharyngitis) is 80% (1-| 4-5 |/5), and being suffered from the disease the feasibility for rhinitis is 50% (1-(4-2)/4).
Processing in data and obtain in the present embodiment in the part of simulation experience, first count the statistical relationship between each symptom and itself and sick type, what then filter out each sick type under given threshold value beta makes a definite diagnosis sympotomatic set. There is provided with reference to diagnostic result according to user the symptom number selected and the difference respectively making a definite diagnosis element number in sympotomatic set in medical diagnosis on disease part.
It should be appreciated that for those of ordinary skills, it is possible to improved according to the above description or converted, and all these are improved and convert the protection domain that all should belong to claims of the present invention.

Claims (6)

1. the automatic assistant diagnosis system of disease based on the big data of medical treatment, it is characterised in that including:
Back-end data memory element, storage has a large amount of existing case data;
Information process unit, specifically includes statistical classification module, makes a definite diagnosis sympotomatic set and ask for module and the automatic diagnostic module of disease, wherein:
Statistical classification module, for obtaining the case data in back-end data memory element, and carries out statistical classification to it, the disease type collection obtaining sympotomatic set with making a definite diagnosis;
Making a definite diagnosis sympotomatic set and ask for module, for the sympotomatic set obtained according to statistical classification module and disease type collection, that asks for various disease makes a definite diagnosis sympotomatic set;
The automatic diagnostic module of disease, for obtaining the disease symptoms data that user provides, generates and selects sympotomatic set, it is compared with the sympotomatic set of making a definite diagnosis of various diseases and calculated, obtains Diseases diagnosis result;
Man-machine interaction unit, for showing that user selects the interface of symptom, and exports the diagnostic result of disease.
2. the automatic aided diagnosis method of disease based on the big data of medical treatment, it is characterised in that comprise the following steps:
S1, statistical classification module obtain the case data in back-end data memory element, and it is carried out the disease type collection that statistical classification obtains sympotomatic set and makes a definite diagnosis, and by make a definite diagnosis sympotomatic set ask for module calculate further obtain various disease make a definite diagnosis sympotomatic set;
S2, user select position and the symptom of disease by man-machine interaction unit, and the data genaration that information process unit provides according to user selects sympotomatic set;
The automatic diagnostic module of S3, disease compares according to selecting sympotomatic set and the sympotomatic set of making a definite diagnosis of each sick type being stored in background data base, calculate and select sympotomatic set and make a definite diagnosis the difference of symptom number in sympotomatic set, with each difference of obtaining for basis for estimation, it is ranked up ascending for difference;
The result of S4, output diagnosis automatically, lists the relevant of first three items difference and makes a definite diagnosis sick type corresponding to sympotomatic set and referential.
3. the automatic aided diagnosis method of disease based on the big data of medical treatment according to claim 2, it is characterised in that obtain in step S1 and make a definite diagnosis sympotomatic set method particularly includes:
Obvious relation between persistence is had, when symptom A often occurs in the case that diagnosis is disease B, it is judged that symptom A is the dominating symptom of disease B between symptom A and disease B; What all dominating symptom of disease B constituted this disease makes a definite diagnosis sympotomatic set.
4. the automatic aided diagnosis method of disease based on the big data of medical treatment according to claim 2, it is characterised in that ask in step S1 and make a definite diagnosis sympotomatic set method particularly includes:
Step a: given threshold value beta, �� >=0.6, symptom sum I, sick type sum J, make count value i=1, j=1;
Step b: statistics symptom ZiWith disease BjBetween dependency C (Zi,Bj), namely it is diagnosed as disease type BjContaining symptom ZiProbability;
Step c: as symptom ZiWith BjBetween disease, dependency is more than threshold value beta, then symptom Z is describediIt is disease BjDominating symptom, by ZiSymptom brings disease B intojMake a definite diagnosis in sympotomatic set, put i=i+1, when i <=I then goes to step b, when i > I then skips to step d;
Step d: synthetic disease BjAll of dominating symptom, the set of composition is disease BjMake a definite diagnosis sympotomatic set, put j=j+1, i=1, as j > J, the process of asking for making a definite diagnosis sympotomatic set terminates, otherwise then forwards step b to.
5. the automatic aided diagnosis method of disease based on the big data of medical treatment according to claim 2, it is characterised in that calculate difference in step S3 method particularly includes:
The symptom that user provides is sent to the automatic diagnostic module of disease by man machine interface, and the retrieval of this module is made a definite diagnosis sympotomatic set by what make a definite diagnosis that sympotomatic set asks for various diseases that module provides, by all disease Bj, j=1,2,3 ..., the difference of the symptom number that the symptom number made a definite diagnosis in sympotomatic set of J and user provide namely | Y (Q (Bj))-Y (G (Z)) | by sorting from small to large;
Wherein, disease BjSympotomatic set of making a definite diagnosis be Q (Bj), the symptom number in this set is Y (Q (Bj)), the set that the symptom that user provides is constituted is G (Z), and the symptom number in this set is Y (G (Z)), | Y (Q (Bj))-Y (G (Z)) | represent the difference of symptom number in two set.
6. the automatic aided diagnosis method of disease based on the big data of medical treatment according to claim 2, it is characterised in that the informative computing formula that in step S4, the sick type of diagnosis is corresponding is:
1-|Y(Q(Bj))-Y(G(Z))|/max(Y(G(Z)),Y(Q(Bj)))
Wherein, disease BjSympotomatic set of making a definite diagnosis be Q (Bj), the symptom number in this set is Y (Q (Bj)); The set that the symptom that patient provides is constituted is G (Z), and the symptom number in this set is Y (G (Z)), max (Y (G (Z)), Y (Q (Bj))) represent take Y (Q (Bj)) and the Y (G (Z)) maximum in both.
CN201511022291.5A 2015-12-31 2015-12-31 Medical big data based disease automatic assistance diagnosis system and method Pending CN105653859A (en)

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CN108335755A (en) * 2017-01-19 2018-07-27 京东方科技集团股份有限公司 Data analysing method and equipment
CN109003670A (en) * 2018-06-29 2018-12-14 武汉圣大东高科技有限公司 Big data medical information processing method, system, terminal device and storage medium
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CN109414177A (en) * 2017-09-11 2019-03-01 深圳市得道健康管理有限公司 Chinese medicine thermal imaging assistant diagnosis system and method based on cloud computing platform
CN109686453A (en) * 2018-12-26 2019-04-26 上海元荷生物技术有限公司 Medical aid decision-making system based on big data
CN110289093A (en) * 2019-05-30 2019-09-27 合肥阿拉丁智能科技有限公司 Medical interrogation Intelligent dialogue system engine
CN112466424A (en) * 2020-12-25 2021-03-09 深圳市健华医疗信息科技有限公司 Auxiliary doctor diagnosis and treatment system
CN112599241A (en) * 2020-12-16 2021-04-02 深圳市唐仁医疗科技有限公司 Intelligent terminal-based big health medical disease auxiliary diagnosis system and method
CN113035350A (en) * 2021-03-29 2021-06-25 北京大学深圳研究生院 Medical decision support method and system based on knowledge extraction
CN113423344A (en) * 2019-02-15 2021-09-21 富士胶片株式会社 Ultrasonic diagnostic apparatus and method for controlling ultrasonic diagnostic apparatus
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CN106126943B (en) * 2016-06-29 2018-06-01 苏州医帮医信息科技有限公司 The state of an illness acquisition method derived based on association symptom
CN106126943A (en) * 2016-06-29 2016-11-16 苏州医帮医信息科技有限公司 The state of an illness acquisition method derived based on association symptom
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CN106202986A (en) * 2016-09-28 2016-12-07 湖南老码信息科技有限责任公司 A kind of tonsillitis Forecasting Methodology based on increment type neural network model and prognoses system
CN108231137A (en) * 2016-12-15 2018-06-29 童综合医疗社团法人童综合医院 A kind of medical system with feedback study
WO2018129911A1 (en) * 2017-01-13 2018-07-19 深圳市前海安测信息技术有限公司 System and method for assisting doctor in diagnosis and treatment
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CN107153775A (en) * 2017-06-13 2017-09-12 京东方科技集团股份有限公司 A kind of intelligence point examines method and device
CN107153775B (en) * 2017-06-13 2020-03-10 京东方科技集团股份有限公司 Intelligent triage method and device
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CN107451416A (en) * 2017-08-28 2017-12-08 昆明理工大学 A kind of sle auxiliary diagnostic equipment and method
CN109414177A (en) * 2017-09-11 2019-03-01 深圳市得道健康管理有限公司 Chinese medicine thermal imaging assistant diagnosis system and method based on cloud computing platform
CN107610774A (en) * 2017-10-25 2018-01-19 医渡云（北京）技术有限公司 Intelligent way of inquisition and device, storage medium, electronic equipment
CN108039206A (en) * 2017-12-21 2018-05-15 北京大学深圳研究生院 A kind of diagnosis and treatment method and system of infectious disease
CN108039206B (en) * 2017-12-21 2021-11-09 北京大学深圳研究生院 Infectious disease diagnosis and treatment method and system
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CN113423344A (en) * 2019-02-15 2021-09-21 富士胶片株式会社 Ultrasonic diagnostic apparatus and method for controlling ultrasonic diagnostic apparatus
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CN112599241A (en) * 2020-12-16 2021-04-02 深圳市唐仁医疗科技有限公司 Intelligent terminal-based big health medical disease auxiliary diagnosis system and method
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