CN104102816A - Symptom match and machine learning-based automatic diagnosis system and method - Google Patents
Symptom match and machine learning-based automatic diagnosis system and method Download PDFInfo
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- 208000024891 symptom Diseases 0.000 title claims abstract description 205
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000010801 machine learning Methods 0.000 title claims abstract description 14
- 238000003745 diagnosis Methods 0.000 title claims abstract description 11
- 201000010099 disease Diseases 0.000 claims abstract description 262
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 262
- 208000011580 syndromic disease Diseases 0.000 claims description 14
- 230000008878 coupling Effects 0.000 claims description 12
- 238000010168 coupling process Methods 0.000 claims description 12
- 238000005859 coupling reaction Methods 0.000 claims description 12
- 230000013011 mating Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 230000003993 interaction Effects 0.000 abstract 1
- 206010012735 Diarrhoea Diseases 0.000 description 21
- 206010000087 Abdominal pain upper Diseases 0.000 description 13
- 210000000436 anus Anatomy 0.000 description 8
- 208000002193 Pain Diseases 0.000 description 6
- 206010006784 Burning sensation Diseases 0.000 description 5
- 235000011389 fruit/vegetable juice Nutrition 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 206010037660 Pyrexia Diseases 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000009089 cytolysis Effects 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 230000007170 pathology Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000035807 sensation Effects 0.000 description 2
- 208000004998 Abdominal Pain Diseases 0.000 description 1
- 206010008631 Cholera Diseases 0.000 description 1
- 206010013954 Dysphoria Diseases 0.000 description 1
- 206010030302 Oliguria Diseases 0.000 description 1
- 206010034568 Peripheral coldness Diseases 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 230000000994 depressogenic effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 210000002700 urine Anatomy 0.000 description 1
- 210000001835 viscera Anatomy 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
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Abstract
The invention discloses a symptom match and machine learning-based automatic diagnosis system and method. The system comprises a disease/symptom database for storing each known disease/symptom and the corresponding symptoms thereof, a user interaction module for receiving a symptom keyword set input by the user, a symptom match module for matching the symptoms in the disease/symptom database according to the symptom keyword set input by the user and calculating the match degree between the symptom keyword set and each disease/symptom, and a diagnosis module for determining the corresponding disease/symptom according to the match degree between the symptom keyword set and each disease/symptom.
Description
Technical field
The present invention relates to medical information field, in particular to a kind of auto-check system and method based on symptom coupling and machine learning.
Background technology
The medical terminology of using in following paper the present invention:
Disease: be that the perverse trend of causing a disease acts on human body, human righteousness resists with it and the body imbalance of yin and yang that causes, internal organs damage, physiological function are not normal or a complete life process of psychological activity obstacle.
Disease: the pathology that is certain one-phase or a certain type in lysis is summarized, generally have one group relatively-stationary, have sings and symptoms inner link, that can disclose disease one-phase or a certain type pathology essence to form.
Symptom: being indivedual, the isolated phenomenon showing in lysis, can be subjective sensation that patient is abnormal or behavior performance can be also the abnormal sign that doctor finds while checking patient.
Along with the raising day by day of the level of informatization, people can obtain medical information by various information terminals, but how according to known symptom offer user accurately disease/disease diagnostic result be still a problem of needing solution badly.
Summary of the invention
The invention provides a kind of auto-check system and method based on symptom coupling and machine learning, in order to offer user's disease/disease diagnostic result accurately according to known symptom.
For achieving the above object, the invention provides a kind of auto-check system based on symptom coupling and machine learning, comprising:
Disease/disease database, for preserving known every kind of disease/disease and corresponding symptom thereof;
User interactive module, for receiving the symptom keyword set of user's input;
Symptom matching module, for mating with the symptom of described disease/disease database according to the described symptom keyword set of user's input, calculates the matching degree of described symptom keyword set and every kind of disease/disease;
Diagnostic module, for determining corresponding disease/disease according to described symptom keyword set with the matching degree of every kind of disease/disease.
Further, said system also comprises:
Vocabulary builds module, for building symptom degree of correlation vocabulary, is specially:
Obtain symptom data, wherein said symptom data comprise the symptom obtained from textbook, dictionary with the symptom set of, near synonym table, every disease/disease obtaining from described disease/disease database and ask every effectively symptom set of request of obtaining record from user;
For obtained symptom data, suppose to have two symptom x and y, the degree of association μ (x, y) of these two symptom x and y is
Wherein ρ (P) represents the judgement weight of data source P, artificially sets ρ (with near synonym table) > ρ (disease/syndrome storehouse) >=ρ (user asks record) according to expertise; R, p, q represents the each syndrome set in data source P;
wherein | p| represents the number of the symptom containing in symptom set p,
Two symptoms that the degree of association is greater than to degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, described symptom matching module comprises:
Weight calculation unit, for calculate the weights W (d, x) of symptom x at disease/disease d according to following formula:
Wherein, ρ (S) represents the weight of data source S, and e represents each description unit information of diseases related/disease in data source S;
Matching degree computing unit, for calculating every disease/disease of described disease/disease database with respect to the matching degree of described symptom keyword set, is specially:
Suppose that the described symptom keyword set that user provides is combined into A, travel through each disease/disease d and corresponding symptom set σ (d) thereof in described disease/disease database;
Matching degree M (A, d) with following formula calculating disease/disease d with respect to described symptom keyword set A:
Press M (A, d) descending order sorts to corresponding disease/disease, the result that sequence is obtained represents with R and presents to user, wherein R={d|M (A, d) > 0 and r (d) < N}, r (d) represents press the sequence number of disease/disease corresponding after the descending sequence of M (A, d), and N is the constant of artificial setting.
Further, said system also comprises:
Update module, for supplementing and upgrading described disease/disease database.
Further, the symptom set of described effective request refers in the matching result of this request and contains disease/disease that matching degree is greater than the constant C of setting.
For achieving the above object, the present invention also provides a kind of automatic diagnosis method based on symptom coupling and machine learning, comprises the following steps:
Receive the symptom keyword set of user's input;
Mate with the symptom in disease/disease database according to the symptom keyword set of user's input, calculate the matching degree of described symptom keyword set and every kind of disease/disease, wherein said disease/disease database is preserved known every kind of disease/disease and corresponding symptom thereof;
Determine corresponding disease/disease according to described symptom keyword set with the matching degree of every kind of disease/disease.
Further, further comprising the steps of before mating step according to the symptom keyword set of user's input with the symptom in disease/disease database:
Build symptom degree of correlation vocabulary, specifically comprise:
Obtain symptom data, wherein said symptom data comprise the symptom obtained from textbook, dictionary with the symptom set of, near synonym table, every disease/disease obtaining from described disease/disease database and ask every effectively symptom set of request of obtaining record from user;
For obtained symptom data, suppose to have two symptom x and y, the degree of association μ (x, y) of these two symptom x and y is
ρ (P) represents the judgement weight of data source P, artificially sets ρ (with near synonym table) > ρ (disease/syndrome storehouse) >=ρ (user asks record) according to expertise; R, p, q represents the each syndrome set in data source P;
wherein | p| represents the number of the symptom containing in symptom set p,
Two symptoms that the degree of association is greater than to degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, the described symptom keyword set according to user's input is mated with the symptom in disease/disease database, and the matching degree step of calculating described symptom keyword set and every kind of disease/disease comprises:
Calculating the weights W (d, x) of symptom x in disease/disease d is
Wherein, ρ (S) represents the weight of data source S,
e represents each description unit information of diseases related/disease in data source S;
Suppose that the described symptom keyword set that user provides is combined into A, travel through each disease/disease d and corresponding symptom set σ (d) thereof in described disease/disease database;
Matching degree M (A, d) with following formula calculating disease/disease d with respect to described symptom keyword set A
Wherein, | A| and | σ (d) | represent respectively the element number in set A and set σ (d);
Press M (A, d) descending order sorts to corresponding disease/disease, the result that sequence is obtained represents with R and presents to user, wherein R={d|M (A, d) > 0 and r (d) < N}, r (d) represents press the sequence number of disease/disease corresponding after the descending sequence of M (A, d), and N is the constant of artificial setting.
Further, said method is further comprising the steps of:
Described disease/disease database is supplemented and upgraded.
Further, the symptom set of described effective request refers in the matching result of this request and contains disease/disease that matching degree is greater than the constant C of setting.
One group of symptom symptom corresponding with the disease of having included in system and syndrome that the present invention provides user is mated, automatically infer and cause possible disease and the syndrome of this group symptom by calculating matching degree, thereby provide relatively accurate diagnostic result to user.
Brief description of the drawings
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the auto-check system module map based on symptom coupling and machine learning of one embodiment of the invention;
Fig. 2 is the auto-check system fundamental diagram based on symptom coupling and machine learning of a preferred embodiment of the invention.
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 clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art, not paying the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
Fig. 1 is the auto-check system module map based on symptom coupling and machine learning of one embodiment of the invention; Fig. 2 is the auto-check system fundamental diagram based on symptom coupling and machine learning of a preferred embodiment of the invention.As shown in the figure, this auto-check system comprises:
Disease/disease database, for preserving known every kind of disease/disease and corresponding symptom thereof;
Wherein, in the time building disease/disease database, selected data source can be national standard (as " tcm clinical practice diagnosis and treatment term disease part "), modern Chinese medicine textbook, modern Chinese medicine dictionary etc., illness, case discussion etc. in traditional Chinese medical science ancient books, and Modern Medical Record data.
User interactive module, for receiving the symptom keyword set of user's input;
Symptom matching module, for mating with the symptom of described disease/disease database according to the described symptom keyword set of user's input, calculates the matching degree of described symptom keyword set and every kind of disease/disease;
Diagnostic module, for determining corresponding disease/disease according to described symptom keyword set with the matching degree of every kind of disease/disease.
Further, said system also comprises:
Vocabulary builds module, for building symptom degree of correlation vocabulary, is specially:
Obtain symptom data, wherein said symptom data comprise the symptom obtained from textbook, dictionary with the symptom set of, near synonym table, every disease/disease obtaining from described disease/disease database and ask every effectively symptom set of request of obtaining record from user; The textbook here, dictionary can be for example traditional Chinese medical science voluminous dictionary, tcm symptom antidiastole, Chinese near synonym dictionary etc., and the symptom set of effective request here refers in the matching result of this request and contains disease/disease that matching degree is greater than the constant C of setting;
For obtained symptom data, suppose to have two symptom x and y, the degree of association μ (x, y) of these two symptom x and y is
Wherein ρ (P) represents the judgement weight of data source P, artificially sets ρ (with near synonym table) > ρ (disease/syndrome storehouse) >=ρ (user asks record) according to expertise; R, p, q represents the each syndrome set in data source P;
wherein | p| represents the number of the symptom containing in symptom set p,
Two symptoms that the degree of association is greater than to degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, described symptom matching module comprises:
Weight calculation unit, for calculate the weights W (d, x) of symptom x at disease/disease d according to following formula:
Wherein, ρ (S) represents the weight of data source S, artificially set according to expertise, meet ρ (national standard) > ρ (textbook, dictionary) >=ρ (traditional Chinese medical science ancient books) > ρ (modern case, e represents each description unit information of diseases related/disease in data source S;
Matching degree computing unit, for calculating every disease/disease of described disease/disease database with respect to the matching degree of described symptom keyword set, is specially:
Suppose that the described symptom keyword set that user provides is combined into A, travel through each disease/disease d and corresponding symptom set σ (d) thereof in described disease/disease database;
Matching degree M (A, d) with following formula calculating disease/disease d with respect to described symptom keyword set A:
Wherein, | A| and | σ (d) | represent respectively the element number in set A and set σ (d);
Press M (A, d) descending order sorts to corresponding disease/disease, the result that sequence is obtained represents with R and presents to user, wherein R={d|M (A, d) > 0 and r (d) < N}, r (d) represents press the sequence number of disease/disease corresponding after the descending sequence of M (A, d), and N is the constant of artificial setting.
Further, said system also comprises:
Update module, for supplementing and upgrading described disease/disease database.
Wherein, can be regularly the opportunity of renewal, as carried out once weekly; Also can be the immediate updating being triggered by accident, as new national standard is promulgated, World Health Organization announces new pandemic information etc.
Example:
User's input: sympotomatic set A: diarrhoea, stomach-ache, anus heat, slippery and rapid pulse
Disease/the disease of including
D
1: the damp and hot wound of having loose bowels
σ (d
1): the stomachache of having loose bowels, rush down that lower urgent, stool being yellowish-brown, burning sensation of the anus, dysphoria with smothery sensation are thirsty, oliguria with yellow urine, slippery and rapid pulse
D
2: cholera humidifier fever
σ (d
2): if the profit of telling, fever and chills bodily pain, depressed, the n and V of fever and chills abdominal pain, ambition, active borhorygmus are not had a pain, are gushed soft stool around navel and continue and rush down clear water or swill juice, peripheral coldness, lower leg contraction, slippery and rapid pulse
Suppose according to data with existing source, by μ (x, y) formula calculates μ (diarrhoea, the stomachache of having loose bowels)=0.14, μ (diarrhoea is rushed down lower urgent)=0.43, μ (stomach-ache, the stomachache of having loose bowels)=0.31, μ (anus heat, burning sensation of the anus)=0.61, μ (slippery and rapid pulse, slippery and rapid pulse)=1.00, μ (stomach-ache, active borhorygmus is had a pain around navel)=0.14, μ (diarrhoea, if gush soft stool continue rush down clear water or swill juice)=0.07, other μ (x, y) is 0;
And according to data with existing source, calculate W (d by the formula of W (d, y)
1, the stomachache of having loose bowels)=0.71, W (d
1, rush down lower urgent)=0.65, W (d
1, burning sensation of the anus)=0.57, W (d
1, slippery and rapid pulse)=0.41, W (d
2, active borhorygmus is had a pain around navel)=0.31, W (d
2if, gush soft stool continue rush down clear water or swill juice)=0.57,, other W (d, y) is 0;
Can calculate d by the formula of M (A, d)
1and d
2as follows with the matching degree of the symptom set of user input respectively:
M (A, d
1μ)=[(diarrhoea, the stomachache of having loose bowels) × W (d
1, the stomachache of having loose bowels) and+μ (diarrhoea is rushed down lower urgent) × W (d
1, rush down lower urgent) and+μ (stomach-ache, the stomachache of having loose bowels) × W (d
1, the stomachache of having loose bowels) and+μ (anus heat, burning sensation of the anus) × W (d
1, burning sensation of the anus) and+μ (slippery and rapid pulse, slippery and rapid pulse) × W (d
1, slippery and rapid pulse)]/(| A|| σ (d
1) |)=0.0485
M (A, d
2μ)=[(stomach-ache, active borhorygmus is had a pain around navel) × W (d
2, active borhorygmus is had a pain around navel)+μ (diarrhoea, if gush soft stool continue rush down clear water or swill juice) × W (d
2if, gush soft stool continue rush down clear water or swill juice)]/(| A|| σ (d
2) |)=0.0021
Due to M (A, d
1) > M (A, d
2), therefore in the diagnostic result R that returns to user, M (A, d
1) be placed on before.Embodiment adapts with said system, is below the automatic diagnosis method embodiment based on symptom coupling and machine learning of one embodiment of the invention, comprises the following steps:
Receive the symptom keyword set of user's input;
Mate with the symptom in disease/disease database according to the symptom keyword set of user's input, calculate the matching degree of described symptom keyword set and every kind of disease/disease, wherein said disease/disease database is preserved known every kind of disease/disease and corresponding symptom thereof;
Determine corresponding disease/disease according to described symptom keyword set with the matching degree of every kind of disease/disease.
Further, further comprising the steps of before mating step according to the symptom keyword set of user's input with the symptom in disease/disease database:
Build symptom degree of correlation vocabulary, specifically comprise:
Obtain symptom data, wherein said symptom data comprise the symptom obtained from textbook, dictionary with the symptom set of, near synonym table, every disease/disease obtaining from described disease/disease database and ask every effectively symptom set of request of obtaining record from user;
For obtained symptom data, suppose to have two symptom x and y, the degree of association μ (x, y) of these two symptom x and y is
ρ (P) represents the judgement weight of data source P, wherein
Two symptoms that the degree of association is greater than to degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
Further, the described symptom keyword set according to user's input is mated with the symptom in disease/disease database, and the matching degree step of calculating described symptom keyword set and every kind of disease/disease comprises:
Calculating the weights W (d, x) of symptom x in disease/disease d is
Wherein, ρ (S) represents the weight of data source S, and e represents each description unit information of diseases related/disease in data source S;
Suppose that the described symptom keyword set that user provides is combined into A, travel through each disease/disease d and corresponding symptom set σ (d) thereof in described disease/disease database;
Matching degree M (A, d) with following formula calculating disease/disease d with respect to described symptom keyword set A
Further, said method is further comprising the steps of:
Described disease/disease database is supplemented and upgraded.
Further, the symptom set of described effective request refers in the matching result of this request and contains disease/disease that matching degree is greater than the constant C of setting.
One group of symptom symptom corresponding with the disease of having included in system and syndrome that the present invention provides user is mated, automatically infer and cause possible disease and the syndrome of this group symptom by calculating matching degree, thereby provide relatively accurate diagnostic result to user.
One of ordinary skill in the art will appreciate that: accompanying drawing is the schematic diagram of an embodiment, the module in accompanying drawing or flow process might not be that enforcement the present invention is necessary.
One of ordinary skill in the art will appreciate that: the module in the device in embodiment can be described and be distributed in the device of embodiment according to embodiment, also can carry out respective change and be arranged in the one or more devices that are different from the present embodiment.The module of above-described embodiment can be merged into a module, also can further split into multiple submodules.
Finally it should be noted that: above embodiment only, in order to technical scheme of the present invention to be described, is not intended to limit; Although the present invention is had been described in detail with reference to previous embodiment, those of ordinary skill in the art is to be understood that: its technical scheme that still can record previous embodiment is modified, or part technical characterictic is wherein equal to replacement; And these amendments or replacement do not make the essence of appropriate technical solution depart from the spirit and scope of embodiment of the present invention technical scheme.
Claims (10)
1. the auto-check system based on symptom coupling and machine learning, is characterized in that, comprising:
Disease/disease database, for preserving known every kind of disease/disease and corresponding symptom thereof;
User interactive module, for receiving the symptom keyword set of user's input;
Symptom matching module, for mating with the symptom of described disease/disease database according to the described symptom keyword set of user's input, calculates the matching degree of described symptom keyword set and every kind of disease/disease;
Diagnostic module, for determining corresponding disease/disease according to described symptom keyword set with the matching degree of every kind of disease/disease.
2. auto-check system according to claim 1, is characterized in that, also comprises:
Vocabulary builds module, for building symptom degree of correlation vocabulary, is specially:
Obtain symptom data, wherein said symptom data comprise the symptom obtained from textbook, dictionary with the symptom set of, near synonym table, every disease/disease obtaining from described disease/disease database and ask every effectively symptom set of request of obtaining record from user;
For obtained symptom data, suppose to have two symptom x and y, the degree of association μ (x, y) of these two symptom x and y is
Wherein (P represents the judgement weight of data source P to ρ, artificially sets ρ (with near synonym table) > ρ (disease/syndrome storehouse) >=ρ (user asks record) according to expertise; R, p, q represents the each syndrome set in data source P;
wherein | p| represents the number of the symptom containing in symptom set p,
Two symptoms that the degree of association is greater than to degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
3. auto-check system according to claim 2, is characterized in that, described symptom matching module comprises:
Weight calculation unit, for calculate the weights W (d, x) of symptom x at disease/disease d according to following formula:
Wherein, ρ (S) represents the weight of data source S, and e represents each description unit information of diseases related/disease in data source S;
Matching degree computing unit, for calculating every disease/disease of described disease/disease database with respect to the matching degree of described symptom keyword set, is specially:
Suppose that the described symptom keyword set that user provides is combined into A, travel through each disease/disease d and corresponding symptom set σ (d) thereof in described disease/disease database;
Matching degree M (A, d) with following formula calculating disease/disease d with respect to described symptom keyword set A:
Wherein, | A| and | σ (d) | represent respectively the element number in set A and set σ (d);
Press M (A, d) descending order sorts to corresponding disease/disease, the result that sequence is obtained represents with R and presents to user, wherein R={d|M (A, d) > 0 and r (d) < N}, r (d) represents press the sequence number of disease/disease corresponding after the descending sequence of M (A, d), and N is the constant of artificial setting.
4. according to the auto-check system described in any one in claim 1-3, it is characterized in that, also comprise:
Update module, for supplementing and upgrading described disease/disease database.
5. auto-check system according to claim 2, is characterized in that, the symptom set of described effective request refers in the matching result of this request and contains disease/disease that matching degree is greater than the constant C of setting.
6. the automatic diagnosis method based on symptom coupling and machine learning, is characterized in that, comprises the following steps:
Receive the symptom keyword set of user's input;
Mate with the symptom in disease/disease database according to the symptom keyword set of user's input, calculate the matching degree of described symptom keyword set and every kind of disease/disease, wherein said disease/disease database is preserved known every kind of disease/disease and corresponding symptom thereof;
Determine corresponding disease/disease according to described symptom keyword set with the matching degree of every kind of disease/disease.
7. automatic diagnosis method according to claim 6, is characterized in that, further comprising the steps of before mating step according to the symptom keyword set of user's input with the symptom in disease/disease database:
Build symptom degree of correlation vocabulary, specifically comprise:
Obtain symptom data, wherein said symptom data comprise the symptom obtained from textbook, dictionary with the symptom set of, near synonym table, every disease/disease obtaining from described disease/disease database and ask every effectively symptom set of request of obtaining record from user;
For obtained symptom data, suppose to have two symptom x and y, the degree of association μ (x, y) of these two symptom x and y is
ρ (P) represents the judgement weight of data source P, artificially sets ρ (with near synonym table) > ρ (disease/syndrome storehouse) >=ρ (user asks record) according to expertise; R, p, q represents the each syndrome set in data source P;
wherein | p| represents the number of the symptom containing in symptom set p,
Two symptoms that the degree of association is greater than to degree of association threshold value are saved in the symptom degree of correlation vocabulary of establishment.
8. automatic diagnosis method according to claim 7, it is characterized in that, the described symptom keyword set according to user's input is mated with the symptom in disease/disease database, and the matching degree step of calculating described symptom keyword set and every kind of disease/disease comprises:
Calculating the weights W (d, x) of symptom x in disease/disease d is
Wherein, ρ (S) represents the weight of data source S, and e represents each description unit information of diseases related/disease in data source S;
Suppose that the described symptom keyword set that user provides is combined into A, travel through each disease/disease d and corresponding symptom set σ (d) thereof in described disease/disease database;
Matching degree M (A, d) with following formula calculating disease/disease d with respect to described symptom keyword set A
Wherein, | A| and | σ (d) | represent respectively the element number in set A and set σ (d);
Press M (A, d) descending order sorts to corresponding disease/disease, the result that sequence is obtained represents with R and presents to user, wherein R={d|M (A, d) > 0 and r (d) < N}, r (d) represents press the sequence number of disease/disease corresponding after the descending sequence of M (A, d), and N is the constant of artificial setting.
9. according to the automatic diagnosis method described in any one in claim 6-8, it is characterized in that, further comprising the steps of:
Described disease/disease database is supplemented and upgraded.
10. automatic diagnosis method according to claim 7, is characterized in that, the symptom set of described effective request refers in the matching result of this request and contains disease/disease that matching degree is greater than the constant C of setting.
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Cited By (31)
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| CN104573350A (en) * | 2014-12-26 | 2015-04-29 | 深圳市前海安测信息技术有限公司 | System and method for general practitioner auxiliary diagnosis and therapy based on network hospital |
| CN104484844A (en) * | 2014-12-30 | 2015-04-01 | 天津迈沃医药技术有限公司 | Self-diagnosis method based on disease circle data information |
| CN104484844B (en) * | 2014-12-30 | 2018-07-13 | 天津迈沃医药技术股份有限公司 | A kind of The Illustrated Family Doctor website platform based on disease circle data information |
| CN106156457A (en) * | 2015-03-27 | 2016-11-23 | 时云医疗科技（上海）有限公司 | A kind of information-pushing method based on sign data and system |
| CN106156457B (en) * | 2015-03-27 | 2019-04-09 | 时云医疗科技（上海）有限公司 | A kind of information-pushing method and system based on sign data |
| CN105335623A (en) * | 2015-12-04 | 2016-02-17 | 上海市第六人民医院 | Bone metastasis cancer bone relevant event judgment system |
| CN107451388A (en) * | 2016-05-31 | 2017-12-08 | 百度（美国）有限责任公司 | For the methods, devices and systems for automating medical diagnosis |
| CN106372439A (en) * | 2016-09-21 | 2017-02-01 | 北京大学 | Method for acquiring and processing disease symptoms and weight knowledge thereof based on case library |
| CN106599574A (en) * | 2016-12-13 | 2017-04-26 | 天津迈沃医药技术股份有限公司 | Diagnosis and treatment data analysis method and system based on medical information platform |
| CN106599574B (en) * | 2016-12-13 | 2019-04-30 | 天津迈沃医药技术股份有限公司 | Diagnosis and treatment data analysing method and system based on medical information platform |
| CN106815479A (en) * | 2017-01-17 | 2017-06-09 | 北京农信互联科技有限公司 | Livestock cultivates remote diagnosis system and method |
| CN107066818A (en) * | 2017-03-30 | 2017-08-18 | 深圳市金立通信设备有限公司 | A kind of methods for the diagnosis of diseases and terminal |
| CN106991284B (en) * | 2017-03-31 | 2019-12-31 | 南华大学 | Intelligent child-care knowledge service method and system |
| CN106991284A (en) * | 2017-03-31 | 2017-07-28 | 南华大学 | Intelligent child-rearing knowledge services method and system |
| CN107680689A (en) * | 2017-05-05 | 2018-02-09 | 平安科技（深圳）有限公司 | Potential disease estimating method, system and the readable storage medium storing program for executing of medical text |
| CN107436996A (en) * | 2017-06-28 | 2017-12-05 | 广州尚恩科技股份有限公司 | A kind of cardiac disease pathological data handling system based on cloud computing |
| CN107330289A (en) * | 2017-07-10 | 2017-11-07 | 叮当（深圳）健康机器人科技有限公司 | A kind of symptom information analysis method and device |
| CN107330287A (en) * | 2017-07-10 | 2017-11-07 | 叮当（深圳）健康机器人科技有限公司 | A kind of disease information analysis method and device |
| CN107330288A (en) * | 2017-07-10 | 2017-11-07 | 叮当（深圳）健康机器人科技有限公司 | A kind of medication information acquisition method and device |
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| CN107463783A (en) * | 2017-08-16 | 2017-12-12 | 安徽影联乐金信息科技有限公司 | A kind of Clinical Decision Support Systems and decision-making technique |
| CN108122611B (en) * | 2017-12-22 | 2021-05-07 | 东软集团股份有限公司 | Information recommendation method and device, storage medium and program product |
| CN108122611A (en) * | 2017-12-22 | 2018-06-05 | 东软集团股份有限公司 | A kind of information recommendation method, device and storage medium, program product |
| CN108630311A (en) * | 2018-04-17 | 2018-10-09 | 杭州希和信息技术有限公司 | A kind of interference method and system of user health |
| CN109036508A (en) * | 2018-05-31 | 2018-12-18 | 平安医疗科技有限公司 | A kind of traditional medical information processing method, device, computer equipment and storage medium |
| CN109036508B (en) * | 2018-05-31 | 2023-10-03 | 平安医疗科技有限公司 | Method and device for processing traditional Chinese medical information, computer equipment and storage medium |
| CN109119132A (en) * | 2018-08-03 | 2019-01-01 | 国家卫生计生委科学技术研究所 | Method and system based on case history characteristic matching monogenic disease title |
| CN111276259A (en) * | 2018-12-04 | 2020-06-12 | 阿里巴巴集团控股有限公司 | Service determination, network interaction, classification method, client, server and medium |
| CN111276259B (en) * | 2018-12-04 | 2024-03-01 | 阿里巴巴集团控股有限公司 | Service determination, network interaction, classification method, client, server and medium |
| CN109949927A (en) * | 2019-02-18 | 2019-06-28 | 四川拾智联兴科技有限公司 | A kind of intelligent diagnosing method and its system based on deep neural network |
| CN110444293A (en) * | 2019-07-30 | 2019-11-12 | 中国中医科学院望京医院 | Caput femoris necrosis diagnosis and therapy system and cloud service system |
| CN111985246A (en) * | 2020-08-27 | 2020-11-24 | 武汉东湖大数据交易中心股份有限公司 | Disease cognitive system based on main symptoms and accompanying symptom words |
| CN111985246B (en) * | 2020-08-27 | 2023-08-15 | 武汉东湖大数据交易中心股份有限公司 | Disease cognitive system based on main symptoms and accompanying symptom words |
| CN112133391A (en) * | 2020-09-17 | 2020-12-25 | 吾征智能技术(北京)有限公司 | Disease cognitive system based on abnormal eyebrows of human body |
| CN112133391B (en) * | 2020-09-17 | 2024-01-26 | 吾征智能技术(北京)有限公司 | Disease cognitive system based on abnormal eyebrows of human body |
| CN112735555A (en) * | 2021-01-20 | 2021-04-30 | 山东第一医科大学附属省立医院(山东省立医院) | Rare disease data acquisition and reporting method and system |
| CN112768082A (en) * | 2021-02-04 | 2021-05-07 | 常熟和医信息技术有限公司 | Method for automatically giving disease diagnosis and treatment scheme according to electronic medical record text |
| CN114140983A (en) * | 2021-12-02 | 2022-03-04 | 内蒙古海洋工信科技有限责任公司 | Household intelligent remote chronic disease supervision system |
| CN114661990A (en) * | 2022-03-23 | 2022-06-24 | 北京百度网讯科技有限公司 | Method, apparatus, device, medium and product for data prediction and model training |
| CN114661990B (en) * | 2022-03-23 | 2024-10-25 | 北京百度网讯科技有限公司 | Method, device, equipment, medium and product for data prediction and model training |
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