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CN104102816B - Auto-check system and method with machine learning is matched based on symptom - Google Patents

Auto-check system and method with machine learning is matched based on symptom Download PDF

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CN104102816B
CN104102816B CN201410280966.5A CN201410280966A CN104102816B CN 104102816 B CN104102816 B CN 104102816B CN 201410280966 A CN201410280966 A CN 201410280966A CN 104102816 B CN104102816 B CN 104102816B
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disease
symptom
user
degree
database
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CN104102816A (en
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唐力
周晋
黄权
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Beijing Shenhuang Technology Co Ltd
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Abstract

The present invention discloses a kind of auto-check system and method matched based on symptom with machine learning, and wherein system includes：Disease/disease database, for preserving known every kind of disease/disease and its corresponding symptom；User interactive module, the symptom keyword set for receiving user's input；Symptom matching module, the symptom keyword set for being inputted according to user is matched with the symptom in the disease/disease database, calculates the matching degree of the symptom keyword set and every kind of disease/disease；Diagnostic module, for determining corresponding disease/disease with the matching degree of every kind of disease/disease according to the symptom keyword set.

Description

Auto-check system and method with machine learning is matched based on symptom
Technical field
The present invention relates to medical information field, in particular to it is a kind of matched based on symptom and machine learning it is automatic Diagnostic system and method.
Background technology
Introduce the medical terminology used in the present invention first below：
Disease：That perverse trend of causing a disease acts on human body, human righteousness resist therewith caused by body imbalance of yin and yang, internal organs group Knit that damage, physiological function be not normal or a complete life process of psychological activity obstacle.
Disease：Be a certain stage or a certain type in lysis pathology summarize, typically have one group it is relatively-stationary, have Inner link, the disease a certain stage can be disclosed or a certain type lesion essence sings and symptoms constitute.
Symptom：It is indivedual, the isolated phenomenons shown in lysis, can is the abnormal subjective sensation of patient or row The abnormal sign found when checking patient for performance or doctor.
With the increasingly raising of the level of informatization, people can obtain medical information by various information terminals, but How the problem of accurate disease of user/disease diagnostic result is still a urgent need to resolve is supplied to according to known symptom.
The content of the invention
The present invention provides a kind of auto-check system and method matched based on symptom with machine learning, to according to known Symptom be supplied to the accurate disease of user/disease diagnostic result.
To reach above-mentioned purpose, the invention provides a kind of auto-check system matched based on symptom with machine learning, Including：
Disease/disease database, for preserving known every kind of disease/disease and its corresponding symptom；
User interactive module, the symptom keyword set for receiving user's input；
Symptom matching module, for the symptom keyword set and the disease/disease data inputted according to user Symptom in storehouse is matched, and calculates the matching degree of the symptom keyword set and every kind of disease/disease；
Diagnostic module, it is corresponding for being determined according to the symptom keyword set and the matching degree of every kind of disease/disease Disease/disease.
Further, said system also includes：
Vocabulary builds module, for building symptom degree of correlation vocabulary, is specially：
Symptom data is obtained, wherein the symptom that the symptom data includes obtaining from textbook, dictionary is same, near synonym Table, the symptom set of the every disease/disease obtained from the disease/disease database and from user request record in obtain The symptom set that every taken effectively asks；
For acquired symptom data, it is assumed that have two symptoms x and y, then two the symptoms x and y degree of association μ (x, Y) it is
Wherein ρ (P) represents data source P judgement weight, is manually set according to expertise, ρ (near synonym table) ＞ ρ (disease/syndrome storehouse) >=ρ (user's request record)；R, p, q represent each syndrome set in data source P；
Wherein | p | the number of symptom contained in symptom set p is represented,
Two symptoms that the degree of association is more than degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, the symptom matching module includes：
Weight calculation unit, for calculating weight Ws (d, x) of the symptom x in disease/disease d according to below equation：
Wherein, ρ (S) represents data source S weight, and e represents to have in data source S each description of related disorders/disease single Metamessage；
Matching degree computing unit, for calculating every disease/disease in the disease/disease database relative to described The matching degree of symptom keyword set, be specially：
Assuming that the symptom keyword set that user provides is combined into A, each in the disease/disease database is traveled through Disease/disease d and its corresponding symptom set σ (d)；
With following formula calculate disease/disease d relative to the symptom keyword set A matching degree M (A, d)：
Wherein, | A | and | σ (d) | set A and set σ (d) is represented respectively In element number；
By M (A, d) descending order corresponding disease/disease is ranked up, by the obtained result R tables of sorting Show and be presented to user, wherein R={ d | M (A, d) ＞ 0 and r (d) ＜ N }, r (d) represent by M (A, it is d) right after descending sequence The sequence number for the disease/disease answered, N is the constant being manually set.
Further, said system also includes：
Update module, for supplementing and updating the disease/disease database.
Further, the symptom set effectively asked refers to containing matching degree be more than in the matching result of the request and set Fixed constant C disease/disease.
To reach above-mentioned purpose, present invention also offers a kind of automatic diagnosis side matched based on symptom with machine learning Method, comprises the following steps：
Receive the symptom keyword set of user's input；
The symptom keyword set inputted according to user is matched with the symptom in disease/disease database, calculates institute The matching degree of symptom keyword set and every kind of disease/disease is stated, wherein the disease/disease database preserves known every Plant disease/disease and its corresponding symptom；
Corresponding disease/disease is determined with the matching degree of every kind of disease/disease according to the symptom keyword set.
Further, carried out in the symptom keyword set inputted according to user with the symptom in disease/disease database It is further comprising the steps of before matching step：
Symptom degree of correlation vocabulary is built, is specifically included：
Symptom data is obtained, wherein the symptom that the symptom data includes obtaining from textbook, dictionary is same, near synonym Table, the symptom set of the every disease/disease obtained from the disease/disease database and from user request record in obtain The symptom set that every taken effectively asks；
For acquired symptom data, it is assumed that have two symptoms x and y, then two the symptoms x and y degree of association μ (x, Y) it is
ρ (P) represents data source P judgement weight, is manually set according to expertise, ρ (near synonym table) ＞ ρ (disease/ Syndrome storehouse) >=ρ (user's request record)；R, p, q represent each syndrome set in data source P；
Wherein | p | the number of symptom contained in symptom set p is represented,
Two symptoms that the degree of association is more than degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, the symptom keyword set inputted according to user is entered with the symptom in disease/disease database Row matching, calculating the matching degree step of the symptom keyword set and every kind of disease/disease includes：
Calculating weight Ws (d, x) of the symptom x in disease/disease d is
Wherein, ρ (S) represents data source S weight,
E represents each description unit information for having related disorders/disease in data source S；
Assuming that the symptom keyword set that user provides is combined into A, each in the disease/disease database is traveled through Disease/disease d and its corresponding symptom set σ (d)；
With following formula calculate disease/disease d relative to the symptom keyword set A matching degree M (A, d)
Wherein, | A | and | σ (d) | the element number in set A and set σ (d) is represented respectively；
By M (A, d) descending order corresponding disease/disease is ranked up, by the obtained result R tables of sorting Show and be presented to user, wherein R={ d | M (A, d) ＞ 0 and r (d) ＜ N }, r (d) represent by M (A, it is d) right after descending sequence The sequence number for the disease/disease answered, N is the constant being manually set.
Further, the above method is further comprising the steps of：
The disease/disease database is supplemented and updated.
Further, the symptom set effectively asked refers to containing matching degree be more than in the matching result of the request and set Fixed constant C disease/disease.
One group of symptom symptom progress corresponding with the disease and syndrome included in system that the present invention provides user Match somebody with somebody, the possible disease and syndrome that cause this group of symptom are inferred automatically by calculating matching degree, so as to provide a user relative Accurate diagnostic result.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the auto-check system module map matched based on symptom with machine learning of one embodiment of the invention；
Fig. 2 works former for the auto-check system based on symptom matching and machine learning of a preferred embodiment of the invention Reason figure.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not paid Embodiment, belongs to the scope of protection of the invention.
Fig. 1 is the auto-check system module map matched based on symptom with machine learning of one embodiment of the invention；Fig. 2 For the auto-check system fundamental diagram matched based on symptom with machine learning of a preferred embodiment of the invention.As schemed Show, the auto-check system includes：
Disease/disease database, for preserving known every kind of disease/disease and its corresponding symptom；
Wherein, build disease/disease database when, selected data source can be national standard (such as《Tcm clinical practice Diagnosis and treatment term disease part》), modern Chinese medicine textbook, modern Chinese medicine dictionary etc., illness, case discussion in Chinese medical book etc., And Modern Medical Record data.
User interactive module, the symptom keyword set for receiving user's input；
Symptom matching module, for the symptom keyword set and the disease/disease data inputted according to user Symptom in storehouse is matched, and calculates the matching degree of the symptom keyword set and every kind of disease/disease；
Diagnostic module, it is corresponding for being determined according to the symptom keyword set and the matching degree of every kind of disease/disease Disease/disease.
Further, said system also includes：
Vocabulary builds module, for building symptom degree of correlation vocabulary, is specially：
Symptom data is obtained, wherein the symptom that the symptom data includes obtaining from textbook, dictionary is same, near synonym Table, the symptom set of the every disease/disease obtained from the disease/disease database and from user request record in obtain The symptom set that every taken effectively asks；Here textbook, dictionary for example can be traditional Chinese medical science voluminous dictionary, tcm symptom discriminating Diagnostics, Chinese near synonym dictionary etc., the symptom set of effective request here refer in the matching result of the request containing Disease/disease of constant C with degree more than setting；
For acquired symptom data, it is assumed that have two symptoms x and y, then two the symptoms x and y degree of association μ (x, Y) it is
Wherein ρ (P) represents data source P judgement weight, is manually set according to expertise, ρ (near synonym table) ＞ ρ (disease/syndrome storehouse) >=ρ (user's request record)；R, p, q represent each syndrome set in data source P；
Wherein | p | the number of symptom contained in symptom set p is represented,
Two symptoms that the degree of association is more than degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, the symptom matching module includes：
Weight calculation unit, for calculating weight Ws (d, x) of the symptom x in disease/disease d according to below equation：
Wherein, ρ (S) represents data source S weight, is manually set according to expertise, meets ρ (national standard) ＞ ρ (religions Section's book, dictionary) >=ρ (Chinese medical books）(modern case, e represents to have in data source S each description of related disorders/disease single to ＞ ρ Metamessage；
Matching degree computing unit, for calculating every disease/disease in the disease/disease database relative to described The matching degree of symptom keyword set, be specially：
Assuming that the symptom keyword set that user provides is combined into A, each in the disease/disease database is traveled through Disease/disease d and its corresponding symptom set σ (d)；
With following formula calculate disease/disease d relative to the symptom keyword set A matching degree M (A, d)：
Wherein, | A | and | σ (d) | the element number in set A and set σ (d) is represented respectively；
By M (A, d) descending order corresponding disease/disease is ranked up, by the obtained result R tables of sorting Show and be presented to user, wherein R={ d | M (A, d) ＞ 0 and r (d) ＜ N }, r (d) represent by M (A, it is d) right after descending sequence The sequence number for the disease/disease answered, N is the constant being manually set.
Further, said system also includes：
Update module, for supplementing and updating the disease/disease database.
Wherein, the opportunity of renewal can be regular, such as once per week；Can also be by accident trigger it is instant Update, such as new national standard is promulgated, World Health Organization announces new pandemic information.
Example：
User inputs：Sympotomatic set A：Diarrhoea, stomach-ache, anus heat, slippery and rapid pulse
Disease/the disease included
d1：The damp and hot wound of diarrhea
σ(d1)：Stomachache of having loose bowels, rush down lower urgent, stool being yellowish-brown, burning sensation of the anus, dysphoria and thirsty, oliguria with yellow urine, slippery and rapid pulse
d2：Cholera humidifier fever
σ(d2)：Tell profit, fever and chills bodily pain, not fever and chills abdominal pain, depressed ambition, nausea and vomiting, active borhorygmus have a pain around navel, If gushing soft stool after rushing down clear water or swill juice, peripheral coldness, lower leg contraction, slippery and rapid pulse
Assuming that according to data with existing source, being calculated by μ (x, y) formula and obtaining μ (diarrhoea, stomachache of having loose bowels)=0.14, μ (diarrhoea is rushed down lower urgent)=0.43, μ (stomach-ache, stomachache of having loose bowels)=0.31, μ (anus heat, burning sensation of the anus)=0.61, μ (diarrhoea gushes soft stool after rushing down to (slippery and rapid pulse, slippery and rapid pulse)=1.00, μ (stomach-ache, active borhorygmus have a pain around navel)=0.14, μ If clear water or swill juice)=0.07, other μ (x, y) are 0；
And according to data with existing source, calculated by W (d, y) formula and obtain W (d1, stomachache of having loose bowels) and=0.71, W (d1, rush down It is lower urgent)=0.65, W (d1, burning sensation of the anus) and=0.57, W (d1, slippery and rapid pulse) and=0.41, W (d2, active borhorygmus have a pain around navel)= 0.31, W (d2If gushing soft stool after rushing down clear water or swill juice)=0.57, other W (d, y) are 0；
Then by M, (A, formula d), which can be calculated, obtains d1And d2The matching degree of the symptom set inputted respectively with user is as follows：
M (A, d1)=[μ (diarrhoea, stomachache of having loose bowels) × W (d1, stomachache of having loose bowels) and+μ (diarrhoea is rushed down lower urgent) × W (d1, Rush down lower urgent)+μ (stomach-ache, stomachache of having loose bowels) × W (d1, stomachache of having loose bowels) and+μ (anus heat, burning sensation of the anus) × W (d1, anus burn Heat)+μ (slippery and rapid pulse, slippery and rapid pulse) × W (d1, slippery and rapid pulse)]/(| A | | σ (d1) |)=0.0485
M (A, d2)=[μ (stomach-ache, active borhorygmus are had a pain around navel) × W (d2, active borhorygmus have a pain around navel)+μ (diarrhoea, If gushing soft stool after rushing down clear water or swill juice) × W (d2If gushing soft stool after rushing down clear water or swill juice)]/(| A | | σ (d2) |)= 0.0021
Due to M (A, d1) ＞ M (A, d2), therefore in the diagnostic result R of user is returned to, M (A, d1) be placed on before. It is adapted with said system embodiment, is below being matched based on symptom for one embodiment of the invention and examining automatically for machine learning Disconnected embodiment of the method, comprises the following steps：
Receive the symptom keyword set of user's input；
The symptom keyword set inputted according to user is matched with the symptom in disease/disease database, calculates institute The matching degree of symptom keyword set and every kind of disease/disease is stated, wherein the disease/disease database preserves known every Plant disease/disease and its corresponding symptom；
Corresponding disease/disease is determined with the matching degree of every kind of disease/disease according to the symptom keyword set.
Further, carried out in the symptom keyword set inputted according to user with the symptom in disease/disease database It is further comprising the steps of before matching step：
Symptom degree of correlation vocabulary is built, is specifically included：
Symptom data is obtained, wherein the symptom that the symptom data includes obtaining from textbook, dictionary is same, near synonym Table, the symptom set of the every disease/disease obtained from the disease/disease database and from user request record in obtain The symptom set that every taken effectively asks；
For acquired symptom data, it is assumed that have two symptoms x and y, then two the symptoms x and y degree of association μ (x, Y) it is
ρ (P) represents data source P judgement weight, wherein
Two symptoms that the degree of association is more than degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, the symptom keyword set inputted according to user is entered with the symptom in disease/disease database Row matching, calculating the matching degree step of the symptom keyword set and every kind of disease/disease includes：
Calculating weight Ws (d, x) of the symptom x in disease/disease d is
Wherein, ρ (S) represents data source S weight, and e represents to have in data source S each description of related disorders/disease single Metamessage；
Assuming that the symptom keyword set that user provides is combined into A, each in the disease/disease database is traveled through Disease/disease d and its corresponding symptom set σ (d)；
With following formula calculate disease/disease d relative to the symptom keyword set A matching degree M (A, d)
By M, (A, d) descending order is to corresponding disease/disease It is ranked up, the result that sequence is obtained is represented with R and is presented to user, wherein R=d | and M (A, d) ＞ 0 and r (d) ＜ N }, r (d) represent by M (A, d) after descending sequence corresponding disease/disease sequence number, N is the constant being manually set.
Further, the above method is further comprising the steps of：
The disease/disease database is supplemented and updated.
Further, the symptom set effectively asked refers to containing matching degree be more than in the matching result of the request and set Fixed constant C disease/disease.
One group of symptom symptom progress corresponding with the disease and syndrome included in system that the present invention provides user Match somebody with somebody, the possible disease and syndrome that cause this group of symptom are inferred automatically by calculating matching degree, so as to provide a user relative Accurate diagnostic result.
One of ordinary skill in the art will appreciate that：Accompanying drawing be module in the schematic diagram of one embodiment, accompanying drawing or Flow is not necessarily implemented necessary to the present invention.
One of ordinary skill in the art will appreciate that：The module in device in embodiment can be according to embodiment description point It is distributed in the device of embodiment, respective change can also be carried out and be disposed other than in one or more devices of the present embodiment.On The module for stating embodiment can be merged into a module, can also be further split into multiple submodule.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used To be modified to the technical scheme described in previous embodiment, or equivalent substitution is carried out to which part technical characteristic；And These modifications are replaced, and the essence of appropriate technical solution is departed from the spirit and model of technical scheme of the embodiment of the present invention Enclose.

Claims (3)

1. a kind of auto-check system matched based on symptom with machine learning, it is characterised in that including：
Disease/disease database, for preserving known every kind of disease/disease and its corresponding symptom；
User interactive module, the symptom keyword set for receiving user's input；
Symptom matching module, for the symptom keyword set that is inputted according to user with the disease/disease database Symptom matched, calculate the matching degree of the symptom keyword set and every kind of disease/disease；
Diagnostic module, for according to the symptom keyword set and the matching degree of every kind of disease/disease determine corresponding disease/ Disease；
Vocabulary builds module, for building symptom degree of correlation vocabulary, is specially：
Obtain symptom data, wherein the symptom data include the symptom that is obtained from textbook, dictionary with, near synonym table, from The symptom set of the every disease/disease obtained in the disease/disease database and ask to obtain in record from user Every symptom set for effectively asking；
For acquired symptom data, it is assumed that have two symptoms x and y, then the degree of association μ (x, y) of two the symptoms x and y are
μ ( x , y ) = Σ P [ ρ ( P ) · Σ r ∈ P g ( r , x , y ) Σ p ∈ P f ( p , x ) · Σ q ∈ P f ( q , y ) ]
Wherein ρ (P) represents data source P judgement weight, is manually set according to expertise, ρ (near synonym table) ＞ ρ (disease/ Syndrome storehouse) >=ρ (user's request record)；R, p, q represent each syndrome set in data source P；
Wherein | p | the number of symptom contained in symptom set p is represented,
Two symptoms that the degree of association is more than degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment；
The symptom matching module includes：
Weight calculation unit, for calculating weight Ws (d, x) of the symptom x in disease/disease d according to below equation：
Wherein, ρ (S) represents data source S weight, and e represents each description unit letter for having related disorders/disease in data source S Breath；
Matching degree computing unit, for calculating every disease/disease in the disease/disease database relative to the symptom The matching degree of keyword set, be specially：
Assuming that the symptom keyword set that user provides is combined into A, each disease in the disease/disease database is traveled through Disease/disease d and its corresponding symptom set σ (d)；
With following formula calculate disease/disease d relative to the symptom keyword set A matching degree M (A, d)：
M ( A , d ) = Σ x ∈ A , y ∈ σ ( d ) μ ( x , y ) · W ( d , y ) | A | · | σ ( d ) | ;
Wherein , ∣ A ∣ are He ∣ σ (d) ∣ represent the element number in set A and set σ (d) respectively；
By M (A, d) descending order corresponding disease/disease is ranked up, the obtained result of sorting is represented simultaneously with R Be presented to user, wherein R={ d | M (A, d) ＞ 0 and r (d) ＜ N }, r (d) represent by M (A, it is d) corresponding after descending sequence The sequence number of disease/disease, N is the constant being manually set.
2. auto-check system according to claim 1, it is characterised in that also include：
Update module, for supplementing and updating the disease/disease database.
3. auto-check system according to claim 1, it is characterised in that the symptom set effectively asked refers to this It is more than the constant C of setting disease/disease in the matching result of request containing matching degree.
CN201410280966.5A 2014-06-20 2014-06-20 Auto-check system and method with machine learning is matched based on symptom Active CN104102816B (en)

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