CN109166619A - Chinese medicine intelligent diagnostics auxiliary system and method based on neural network algorithm - Google Patents
Chinese medicine intelligent diagnostics auxiliary system and method based on neural network algorithm Download PDFInfo
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Abstract
The invention discloses a kind of Chinese medicine intelligent assistance system and method based on neural network algorithm, Chinese medicine intelligent assistance system based on neural network algorithm, the system comprises: diagnostic message collects conversion module: the relevant symptom information of diagnosis for collecting patient main suit, and is cut into descriptive symptom language；Symptom matching module: for being converted to standardized sympotomatic set；Symptom weight calculation module: for calculating the identification weighted value of various symptom；Diagnosis characteristic of disease disease bit vector module: for calculating diagnosis characteristic of disease disease bit vector；Disease type diagnostic module: for obtaining the main disease type of the diagnosis；Prescription recommending module: for recommending prescription according to disease type.The strength of association of chief complaint language disease of tcm of the present invention by storage in the database, the possibility Chinese medicine disease type of patient can be quickly determined by a series of analysis method, then treatment prescription out is quickly recommended according to Chinese medicine disease type symptom, doctor is facilitated more accurately to diagnose, reliability is higher.
Description
Technical field
The present invention relates to Chinese medicine technical field of information processing, and in particular to a kind of Chinese medicine intelligence based on neural network algorithm
Auxiliary system and method.
Background technique
With the fast development based on data and information system, how to pass through information-based means and quickly improve primary care
The quality of medical care of unit by be the following basic medical unit and patient core demand.The four methods of diagnosis that system is acquired by realizing doctor
Use of the degree of association and algorithm of information and Chinese medical disease etc. solves the limitation of clinic doctor's self-technique, and with suggestion at
Side releases patient's pain.Currently, predominantly staying in theoretic or these technologies for the correlation research of main suit and disease
It can only be solved the problems, such as with method some portion of.
Summary of the invention
The purpose of the present invention is to provide a kind of Chinese medicine intelligent assistance system and method based on neural network algorithm solve
It how to be different medical unit aiming at the problem that disease of tcm quickly recommends treatment prescription out by information-based means.
In order to solve the above technical problems, the invention adopts the following technical scheme:
A kind of Chinese medicine intelligent assistance system based on neural network algorithm, the system comprises:
Diagnostic message collects conversion module: the relevant symptom information of diagnosis for collecting patient main suit, and according to symptom information
Natural language word cutting is executed, symptom information is cut into descriptive symptom language；
Symptom matching module: symptom language and symptom for being obtained diagnostic message collection conversion module using neural network algorithm
Repertorie is matched, and is converted to standardized sympotomatic set；
Symptom weight calculation module: the symptom disease type stored in the sympotomatic set and database for being obtained according to symptom matching module
Image calculates the identification weighted value of various symptom；
Diagnosis characteristic of disease disease bit vector module: for passing through mind with characteristic of disease disease position when diagnosis according to the identification weighted value of various symptom
Diagnosis characteristic of disease disease bit vector is calculated through network algorithm；
Disease type diagnostic module: by the weighted value according to the Chinese medicine disease type saved in database and classical symptom with symptom weight based on
The entire identification weighted value of the format that calculation module obtains carries out integration calculating, and is arranged according to the weight summation in each disease type
Name, obtains the main disease type of the diagnosis；
Prescription recommending module: it for finding out corresponding all prescriptions by main disease type, then searches and is somebody's turn to do from database
Then the characteristic of disease disease bit vector of prescription examines the characteristic of disease disease bit vector of the prescription with obtained in diagnosis characteristic of disease disease bit vector module
Disconnected characteristic of disease disease bit vector carries out similarity mode, finally using the highest prescription of matching degree as recommendation.
Preferably, the system also includes data memory module, for storing Chinese medicine disease type, Chinese medicine disease type and symptom
All prescriptions corresponding to image relationship, Chinese medicine disease type, characteristic of disease disease bit vector corresponding to prescription.
Preferably, all prescriptions corresponding to the Chinese medicine disease type are that the pre- clinical medical data for first passing through magnanimity carries out
Quantitative analysis, and provide data by the clinical medical data of magnanimity and support.
Preferably, the characteristic of disease disease position in the characteristic of disease disease bit vector is the clinical medical data progress previously according to magnanimity
Normalized by definition.
A kind of Chinese medicine intelligence householder method based on neural network algorithm, comprising the following steps:
S1: diagnostic message collects conversion module and collects the relevant symptom information of diagnosis of patient main suit, and is held according to symptom information
Symptom information is cut into descriptive symptom language by row natural language word cutting；
S2: the symptom language and symptom language that symptom matching module is obtained diagnostic message collection conversion module using neural network algorithm
Library is matched, and is converted to standardized sympotomatic set；
S3: the symptom disease type stored in the sympotomatic set and database that symptom weight calculation module is obtained according to symptom matching module reflects
Identification weighted value as calculating various symptom；
S4: diagnosis characteristic of disease disease bit vector module passes through nerve with characteristic of disease disease position when diagnosis according to the identification weighted value of various symptom
Network algorithm calculates the characteristic of disease disease bit vector of diagnosis；
S5: disease type diagnostic module is according to the weighted value of the Chinese medicine disease type saved in database and classical symptom with symptom weight calculation
The entire identification weighted value of the format that module obtains carries out integration calculating, and carries out ranking according to the weight summation in each disease type,
Obtain the main disease type of the diagnosis；
S6: prescription recommending module finds out corresponding all prescriptions by main disease type, then obtains from this from searching in database
Then the characteristic of disease disease bit vector of side will diagnose obtained in the characteristic of disease disease bit vector of the prescription and diagnosis characteristic of disease disease bit vector module
Characteristic of disease disease bit vector carry out similarity mode, finally using the highest prescription of matching degree as recommendation.
Preferably, the S3 step calculates the identification weighted value of various symptom, calculation formula using TF-IDF algorithm
It is: TF-IDF (symptom, disease type)=(symptom comes across the frequency/card type of the disease type in the Symptomatic total frequency of institute) *
LOG (the disease type number/symptom of 1+ in total appears in how many disease types).
Preferably, the S6 step obtains after recommending prescription further include:
S7: it is searched from database according to obtained recommendation prescription and obtains the characteristic of disease disease bit vector of the prescription, then by the prescription
Characteristic of disease disease bit vector and the characteristic of disease disease bit vector of diagnosis compare, and prompt variant between two kinds of characteristic of disease disease bit vectors
Characteristic of disease disease position, be finely adjusted for diagnostician.
Compared with prior art, the beneficial effects of the present invention are:
The strength of association of chief complaint language disease of tcm of the present invention by storage in the database, passes through a series of analysis side
Method can quickly determine the possibility Chinese medicine disease type symptom of patient, then quickly recommend treatment prescription out according to Chinese medicine disease type symptom,
The insufficient doctor of experience is facilitated more accurately to diagnose, reliability is higher.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
For one embodiment of system of the invention, a kind of Chinese medicine intelligent assistance system based on neural network algorithm,
The system comprises:
Diagnostic message collects conversion module: the relevant symptom information of diagnosis for collecting patient main suit, and according to symptom information
Natural language word cutting is executed, symptom information is cut into descriptive symptom language；
Symptom matching module: symptom language and symptom for being obtained diagnostic message collection conversion module using neural network algorithm
Repertorie is matched, and is converted to standardized sympotomatic set；
Symptom weight calculation module: the symptom disease type stored in the sympotomatic set and database for being obtained according to symptom matching module
Image calculates the identification weighted value of various symptom；
Diagnosis characteristic of disease disease bit vector module: for passing through mind with characteristic of disease disease position when diagnosis according to the identification weighted value of various symptom
Diagnosis characteristic of disease disease bit vector is calculated through network algorithm；
Disease type diagnostic module: by the weighted value according to the Chinese medicine disease type saved in database and classical symptom with symptom weight based on
The entire identification weighted value of the format that calculation module obtains carries out integration calculating, and is arranged according to the weight summation in each disease type
Name, obtains the main disease type of the diagnosis；
Prescription recommending module: it for finding out corresponding all prescriptions by main disease type, then searches and is somebody's turn to do from database
Then the characteristic of disease disease bit vector of prescription examines the characteristic of disease disease bit vector of the prescription with obtained in diagnosis characteristic of disease disease bit vector module
Disconnected characteristic of disease disease bit vector carries out similarity mode, finally using the highest prescription of matching degree as recommendation.
Further, for another embodiment of system of the invention, the system also includes data memory modules, use
All prescriptions corresponding to storage Chinese medicine disease type, the image relationship of Chinese medicine disease type and symptom, Chinese medicine disease type, corresponding to prescription
Characteristic of disease disease bit vector.
Further, for another embodiment of system of the invention, all prescriptions corresponding to the Chinese medicine disease type
It is that the pre- clinical medical data for first passing through magnanimity carries out quantitative analysis, and provides data branch by the clinical medical data of magnanimity
It holds.
Further, the characteristic of disease disease position for another embodiment of system of the invention, in the characteristic of disease disease bit vector
It is to be standardized definition previously according to the clinical medical data of magnanimity.
Referring to Fig. 1, for one embodiment of method of the invention, a kind of Chinese medicine based on neural network algorithm is intelligently auxiliary
Aid method, comprising the following steps:
S1: diagnostic message collects conversion module and collects the relevant symptom information of diagnosis of patient main suit, and is held according to symptom information
Symptom information is cut into descriptive symptom language by row natural language word cutting；
S2: the symptom language and symptom language that symptom matching module is obtained diagnostic message collection conversion module using neural network algorithm
Library is matched, and is converted to standardized sympotomatic set；
S3: the symptom disease type stored in the sympotomatic set and database that symptom weight calculation module is obtained according to symptom matching module reflects
Identification weighted value as calculating various symptom；
S4: diagnosis characteristic of disease disease bit vector module passes through nerve with characteristic of disease disease position when diagnosis according to the identification weighted value of various symptom
Network algorithm calculates the characteristic of disease disease bit vector of diagnosis；
S5: disease type diagnostic module is according to the weighted value of the Chinese medicine disease type saved in database and classical symptom with symptom weight calculation
The entire identification weighted value of the format that module obtains carries out integration calculating, and carries out ranking according to the weight summation in each disease type,
Obtain the main disease type of the diagnosis；
S6: prescription recommending module finds out corresponding all prescriptions by main disease type, then obtains from this from searching in database
Then the characteristic of disease disease bit vector of side will diagnose obtained in the characteristic of disease disease bit vector of the prescription and diagnosis characteristic of disease disease bit vector module
Characteristic of disease disease bit vector carry out similarity mode, finally using the highest prescription of matching degree as recommendation.
Further, for another embodiment of method of the invention, the S3 step is calculated using TF-IDF algorithm
The identification weighted value of various symptom, calculation formula is: TF-IDF (symptom, disease type)=(symptom comes across the frequency of the disease type
Secondary/card the type Symptomatic total frequency) * LOG (the disease type number/symptom of 1+ in total appears in how many disease types).?
Eyespot inherently sets out but because with this non-linear formula institute to simulate the so-called degree of discrimination in the formula using TFIDF
The value calculated, in range, have it is exponential, therefore can with calculate after TF-IDF size, make sequence
As a result, and limit range between 0 to 1, as a kind of means for standardizing Normalization, it can finally obtain this
Identify the matrix of weighted value.
Further, for another embodiment of method of the invention, the S6 step obtains also wrapping after recommending prescription
It includes:
S7: it is searched from database according to obtained recommendation prescription and obtains the characteristic of disease disease bit vector of the prescription, then by the prescription
Characteristic of disease disease bit vector and the characteristic of disease disease bit vector of diagnosis compare, and prompt variant between two kinds of characteristic of disease disease bit vectors
Characteristic of disease disease position, be finely adjusted for diagnostician.A disparity range may be selected, then prompt doctor more than this range, for doctor
Life is finely adjusted.
By way of this fine tuning, prescription can be finely adjusted according to the experience of doctor, during continuous fine tuning
Prescription can be continued to optimize.
Otherwise for complicated illness, the disease type diagnostic module in above system is also used to according to master matched in the module
The identification weight analysis of disease disease type and various symptom goes out the minor symptom disease type of primary symptom disease type appearance.
When the clinical more complicated state of an illness, it can find the diagnosis of only primary symptom, be that can not go to explain all well
The sympotomatic set that input is come in, at this moment component can try to find a minor symptom (second opinion of similar doctor trained in Western medicine) again, so as to same
When use primary symptom and minor symptom when, can preferably explain faced clinical state.
Primary symptom and minor symptom can cover one group of complete this symptom group of symptom group clinically jointly and can be updated to instructs in advance
The neural network for the symptom verification element perfected, can go and meet again to obtain characteristic of disease disease bit vector this vector
Each prescription vector in candidate prescription library within the scope of main symptom does the similarity mode based on COS value so as to can be in candidate
In prescription, after filtering out most suitable prescription.In fact be exactly two inner product values with dimensional vector, i.e., cos value be inner product divided by
Span from.
Application examples one: by one group of symptom, judge that chill violates the Syndrome in TCM of lung by intelligent auxiliary diagnosis system.
S1: user (doctor) issues one group of four methods of diagnosis information of starting: [cough, coughs up a small amount of dilute white phlegm, and asthma is micro- to have aversion to cold
Fever, nasal obstruction, watery nasal discharge, larynx is itched, or sees that bodily pain is lossless, thin white fur of tongue, floating and tense pulse]；StandardSyndrom component is receiving
To after patient's four methods of diagnosis information, by by step S1 Lai doctor input information, be cut into symptom language [(cough descriptive one by one
Cough) (coughing up a small amount of dilute white phlegm) (asthma) (micro- to have fever with aversion to cold) (nasal obstruction) (watery nasal discharge) (larynx is itched) (or seeing that bodily pain is lossless) (tongue fur
Bao Bai) (floating and tense pulse)].
Next S2:StandardSyndrom component can utilize neural network algorithm, go to calibrate most suitable matched standard
Symptom word: [(cough) (it is dilute that phlegm lacks matter) (phlegm color is white) (asthma) (aversion to cold) (fever) (nasal obstruction) (thin nasal discharge) (larynx is itched)
(bodily pain) (lossless) (tongue fur is thin) (whitish tongue) (floating pulse) (arteries and veins is tight)].
S3:DiagnosisSystem component receive the standardized sympotomatic set that is passed over from StandardSyndrom it
Afterwards, the identification weighted value of various symptom is calculated according to magnanimity case data.
S4:DiagnosisSystem component using characteristic of disease disease position neural network model determine come vector, can be ('
Cold ', 0.97), (' exogenous wind ', 0.92), (' table ', 0.86), (' lung ', 0.80), (' close ', 0.75), (' phlegm ',
0.64), (' the circulation of vital energy in the wrong direction ', 0.49).
S5:DiagnosisSystem component meeting basis from the distinguished veteran doctors of TCM case data of magnanimity, learns to each in advance
The weighted value of formula Syndrome in TCM and each classical symptom removes the identification weighted value with the various symptom of S4, does integration and calculates at this moment, i.e.,
At this moment more than can identify in system nearly 400 cards can be removed to make the ranking. on the basis of summation weight, the Top3 of system
Ranking be respectively 1. syndrome of wind-cold attacking lung .2. syndrome of superficies tightened by wind-cold .3. cold closed lungs card because Chinese medicine card type from each other
It is relevance, therefore system finds out most probable three cards, meaning clinically can be closer to, sometimes the difference in degree
It is different, if is sometimes a shade of difference fallen ill on sick position between syndrome of wind-cold attacking lung and cold closed lung card, such as syndrome of wind-cold attacking lung and chill
This case of 5.2 between beam exterior syndrome, because single main symptom can explain the nearly all symptom clinically seen, institute well
It is i.e. enough that a main symptom is also stayed at conclusion.
Master of the S6:PrescriptionDecision component on DiagnosisSystem component clearly good Chinese medicine
Card look for for after syndrome of wind-cold attacking lung, this component can be candidate in the available prescription under this Chinese medicine main symptom；Master is belonged to all
When card is the prescription library row search of syndrome of wind-cold attacking lung, PrescriptionDecision component can go to utilize this group of four methods of diagnosis information
The characteristic of disease disease bit vector of representative diagnosis is done with the characteristic of disease disease bit vector of prescription effect of all available prescription candidates and is matched
It calculates, finally by the highest prescription of matching degree；To this recommend prescription, Sanao tang plus-minus, vector calculate, being will be below
Drug forms (aster 9g, Fructus Aurantii 9g, dried orange peel 9g, almond 9g, Chinese ephedra 9g, tuber of pinellia 9g, campanulaceae 9g, Radix Glycyrrhizae 9g), is updated to and is based on
Composition and effect of tens of thousands of head prescriptions and the neural network model after having learnt, and obtain prescription effect characteristic of disease disease bit vector "
Exogenous wind ": 0.98, " closing ": 0.52, " stagnation of the circulation of vital energy ": 0.39, " lung ": 0.82, " phlegm ": 0.77, " cold ": 0.92 }.
Application examples two: by one group of symptom, the clinically complicated state of an illness is judged by intelligent auxiliary diagnosis system, needs main symptom
And after secondary card merges, the case of ability complete diagnosis.
S1: user (doctor) issues one group of four methods of diagnosis information of starting, and [glossolalia, dizzy, headache, tired, tongue is strong
Language is not smoothgoing, hemiplegia, nausea and vomiting, and feeling of fullness uncomfortable in chest, dry, bitter taste is indigestion and loss of appetite, dry and hard excrement, and right side upper and lower extremities muscular strength weakens, right
Side upper and lower extremities limitation of activity, the right skew of tongue body, red tongue body, tongue is yellow thick greasy, and body is fat]；StandardSyndrom component is connecing
After receiving patient's four methods of diagnosis information, by by step 1 doctor input information, be cut into symptom language descriptive one by one.
Next S 2:StandardSyndrom component can utilize neural network algorithm, remove to calibrate most suitable matched mark
Quasi- symptom word [(dislike by (glossolalia), (dizziness), (headache), (out of strength), (the strong language of tongue is stuttering), (hemiplegia)
The heart), (vomiting), (chest gastral cavity ruffian), (dry), (bitter taste), (loss of appetite), (dry and hard excrement), (tongue is askew), (tongue
It is red), (yellow tongue fur), (tongue fur is greasy), (tongue fur is thick), (body is fat)].
S3:DiagnosisSystem component receive the standardized sympotomatic set that is passed over from StandardSyndrom it
Afterwards, the identification weighted value of various symptom is calculated according to magnanimity case data.
S4:DiagnosisSystem component using characteristic of disease disease position neural network model determine come vector, can be [('
The dynamic wind of phlegm ', 0.71), (' heat ', 0.47), (' large intestine ', 0.36), (' liver ', 0.27), (' ', 0.24), (' head ',
0.14), (' blood stasis ', 0.09), (' wet ', 0.08), (' the circulation of vital energy in the wrong direction ', 0.08)].
S5: system can be found after the classical symptom group that Integrated Summary input is come in and various card and the weighted value of symptom
The Syndrome Scale that single main symptom can be explained be inadequate at this moment, system can try to as each main symptom in the top,
Possible time card is looked for so as to the main symptom and secondary card that make collocation get up, can be high on comprehensive weight, and can solve jointly
The Syndrome Scale released be enough with this case study on implementation for, the ranking of Top3 is respectively that 1. syndrome of wind-phlegm invading collaterals wet poisons are accumulate
The first of knot card 2. syndrome of phlegm-heat attacking internally, 3. syndrome of wind-phlegm invading upward syndrome of hyperactivity of fire due to yin deficiency system is recommended and third is recommended, and is all to diagnose
In conclusion, when such as syndrome of wind-phlegm invading collaterals, some can be found this is because system is using a main symptom with main symptom and time card
It to wet or malicious relevant symptom group, can not effectively be explained, therefore can try to find out syndrome of accumulated dampness-toxicity again, this time
Card comes out and arranges in pairs or groups.
Master of the S6:PrescriptionDecision component on DiagnosisSystem component clearly good Chinese medicine
Card look for for after syndrome of wind-phlegm invading collaterals, this component can be candidate in the available prescription under this Chinese medicine main symptom；In search,
PrescriptionDecision component can utilize same algorithm again, finally by the highest prescription of matching degree, to this recommendation
Prescription, Wendan Tang, from quasi- square source: the vector that Guo Zhen ball name cures case calculates, be by drug below composition (calamus 8g,
Rhizoma pinellinae praeparata 8g, Radix Curcumae 10g, Exocarpium Citri Rubrum 10g, Fructus Aurantii 10g, Poria cocos 12g, caulis bambusae in taenian 12g, bombyx batryticatus 12g, Caulis Spatholobi 12g, lycopodium calvatum 12g,
Radix Angelicae Sinensis 15g), it is updated to composition and effect based on tens of thousands of first prescriptions and the neural network model after having learnt, and obtain prescription
Characteristic of disease disease bit vector " dynamic wind ": 0.46, " closing ": 0.17, " stagnation of the circulation of vital energy ": 0.13, " heat ": 0.46, " phlegm ": 0.94, " wet ":
0.28, " blood stasis ": 0.22, " liver ": 0.12 }.
" one embodiment ", " another embodiment ", " embodiment ", " preferred implementation spoken of in the present specification
Example " etc., referring to combining specific features, structure or the feature of embodiment description includes describing extremely in the application generality
In few one embodiment.It is not centainly to refer to the same embodiment that statement of the same race, which occur, in multiple places in the description.Into one
For step, when describing a specific features, structure or feature in conjunction with any embodiment, what is advocated is to combine other implementations
Example realizes that this feature, structure or feature are also fallen within the scope of the present invention.
Although reference be made herein to invention has been described for multiple explanatory embodiments of the invention, however, it is to be understood that
Those skilled in the art can be designed that a lot of other modification and implementations, these modifications and implementations will fall in this Shen
It please be within disclosed scope and spirit.More specifically, disclose in the application, drawings and claims in the range of, can
With the building block and/or a variety of variations and modifications of layout progress to theme combination layout.In addition to building block and/or layout
Outside the modification and improvement of progress, to those skilled in the art, other purposes also be will be apparent.
Claims (7)
1. a kind of Chinese medicine intelligent assistance system based on neural network algorithm, it is characterised in that: the system comprises:
Diagnostic message collects conversion module: the relevant symptom information of diagnosis for collecting patient main suit, and according to symptom information
Natural language word cutting is executed, symptom information is cut into descriptive symptom language；
Symptom matching module: symptom language and symptom for being obtained diagnostic message collection conversion module using neural network algorithm
Repertorie is matched, and is converted to standardized sympotomatic set；
Symptom weight calculation module: the symptom disease type stored in the sympotomatic set and database for being obtained according to symptom matching module
Image calculates the identification weighted value of various symptom；
Diagnosis characteristic of disease disease bit vector module: for passing through mind with characteristic of disease disease position when diagnosis according to the identification weighted value of various symptom
Diagnosis characteristic of disease disease bit vector is calculated through network algorithm；
Disease type diagnostic module: by the weighted value according to the Chinese medicine disease type saved in database and classical symptom with symptom weight based on
The entire identification weighted value of the format that calculation module obtains carries out integration calculating, and is arranged according to the weight summation in each disease type
Name, obtains the main disease type of the diagnosis；
Prescription recommending module: it for finding out corresponding all prescriptions by main disease type, then searches and is somebody's turn to do from database
Then the characteristic of disease disease bit vector of prescription examines the characteristic of disease disease bit vector of the prescription with obtained in diagnosis characteristic of disease disease bit vector module
Disconnected characteristic of disease disease bit vector carries out similarity mode, finally using the highest prescription of matching degree as recommendation.
2. the Chinese medicine intelligent assistance system according to claim 1 based on neural network algorithm, it is characterised in that: the system
System further includes data memory module, for storing corresponding to the image relationship of Chinese medicine disease type, Chinese medicine disease type and symptom, Chinese medicine disease type
All prescriptions, characteristic of disease disease bit vector corresponding to prescription.
3. the Chinese medicine intelligent assistance system according to claim 2 based on neural network algorithm, it is characterised in that: in described
All prescriptions corresponding to doctor's disease type are that the pre- clinical medical data for first passing through magnanimity carries out quantitative analysis, and facing by magnanimity
Bed medical data provides data and supports.
4. the Chinese medicine intelligent assistance system according to claim 2 based on neural network algorithm, it is characterised in that: the disease
Characteristic of disease disease position in venereal disease bit vector is to be standardized definition previously according to the clinical medical data of magnanimity.
5. a kind of Chinese medicine intelligence householder method based on neural network algorithm, it is characterised in that: the following steps are included:
S1: diagnostic message collects conversion module and collects the relevant symptom information of diagnosis of patient main suit, and is held according to symptom information
Symptom information is cut into descriptive symptom language by row natural language word cutting；
S2: the symptom language and symptom language that symptom matching module is obtained diagnostic message collection conversion module using neural network algorithm
Library is matched, and is converted to standardized sympotomatic set；
S3: the symptom disease type stored in the sympotomatic set and database that symptom weight calculation module is obtained according to symptom matching module reflects
Identification weighted value as calculating various symptom；
S4: diagnosis characteristic of disease disease bit vector module passes through nerve with characteristic of disease disease position when diagnosis according to the identification weighted value of various symptom
Network algorithm calculates the characteristic of disease disease bit vector of diagnosis；
S5: disease type diagnostic module is according to the weighted value of the Chinese medicine disease type saved in database and classical symptom with symptom weight calculation
The entire identification weighted value of the format that module obtains carries out integration calculating, and carries out ranking according to the weight summation in each disease type,
Obtain the main disease type of the diagnosis；
S6: prescription recommending module finds out corresponding all prescriptions by main disease type, then obtains from this from searching in database
Then the characteristic of disease disease bit vector of side will diagnose obtained in the characteristic of disease disease bit vector of the prescription and diagnosis characteristic of disease disease bit vector module
Characteristic of disease disease bit vector carry out similarity mode, finally using the highest prescription of matching degree as recommendation.
6. the Chinese medicine intelligence householder method according to claim 5 based on neural network algorithm, it is characterised in that: the S3
Step calculates the identification weighted value of various symptom using TF-IDF algorithm, and calculation formula is: TF-IDF (symptom, disease type)=
(symptom come across the frequency/card type of the disease type Symptomatic total frequency) * LOG (the disease type number/disease of 1+ in total
Shape appears in how many disease types).
7. the Chinese medicine intelligence householder method according to claim 5 based on neural network algorithm, it is characterised in that: the S6
Step obtains after recommending prescription further include:
S7: it is searched from database according to obtained recommendation prescription and obtains the characteristic of disease disease bit vector of the prescription, then by the prescription
Characteristic of disease disease bit vector and the characteristic of disease disease bit vector of diagnosis compare, and prompt variant between two kinds of characteristic of disease disease bit vectors
Characteristic of disease disease position, be finely adjusted for diagnostician.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110085313A (en) * | 2019-04-08 | 2019-08-02 | 皮敏 | A kind of Database Systems based on intelligent Chinese medicine robot |
| CN110335684A (en) * | 2019-06-14 | 2019-10-15 | 电子科技大学 | The intelligent dialectical aid decision-making method of Chinese medicine based on topic model technology |
| CN111091906A (en) * | 2019-10-31 | 2020-05-01 | 中电药明数据科技(成都)有限公司 | Auxiliary medical diagnosis method and system based on real world data |
| CN111462895A (en) * | 2020-03-30 | 2020-07-28 | 安徽科大讯飞医疗信息技术有限公司 | Auxiliary diagnosis method and system |
| CN111599486A (en) * | 2020-05-12 | 2020-08-28 | 成都睿明医疗信息技术有限公司 | Traditional Chinese medicine prescription recommendation sorting method based on data matching |
| CN111816305A (en) * | 2020-08-05 | 2020-10-23 | 沈国忠 | Cold Chinese patent medicine recommendation method based on artificial intelligence |
| CN112185503A (en) * | 2020-09-14 | 2021-01-05 | 深圳金草健康科技有限公司 | Intelligent auxiliary diagnosis system and method for traditional Chinese medicine |
| CN112420191A (en) * | 2020-11-23 | 2021-02-26 | 北京麦岐科技有限责任公司 | Traditional Chinese medicine auxiliary decision making system and method |
| CN113241173A (en) * | 2021-05-12 | 2021-08-10 | 华中科技大学 | Traditional Chinese medicine auxiliary diagnosis and treatment method and system for chronic obstructive pulmonary disease |
| CN114628001A (en) * | 2022-03-16 | 2022-06-14 | 平安科技（深圳）有限公司 | Prescription recommendation method, system, equipment and storage medium based on neural network |
| CN115424696A (en) * | 2022-11-04 | 2022-12-02 | 之江实验室 | Traditional Chinese medicine rare disease traditional Chinese medicine prescription generation method and system based on transfer learning |
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110085313A (en) * | 2019-04-08 | 2019-08-02 | 皮敏 | A kind of Database Systems based on intelligent Chinese medicine robot |
| CN110335684A (en) * | 2019-06-14 | 2019-10-15 | 电子科技大学 | The intelligent dialectical aid decision-making method of Chinese medicine based on topic model technology |
| CN111091906B (en) * | 2019-10-31 | 2023-06-20 | 中电药明数据科技(成都)有限公司 | Auxiliary medical diagnosis method and system based on real world data |
| CN111091906A (en) * | 2019-10-31 | 2020-05-01 | 中电药明数据科技(成都)有限公司 | Auxiliary medical diagnosis method and system based on real world data |
| CN111462895A (en) * | 2020-03-30 | 2020-07-28 | 安徽科大讯飞医疗信息技术有限公司 | Auxiliary diagnosis method and system |
| CN111462895B (en) * | 2020-03-30 | 2024-04-05 | 讯飞医疗科技股份有限公司 | Auxiliary diagnosis method and system |
| CN111599486A (en) * | 2020-05-12 | 2020-08-28 | 成都睿明医疗信息技术有限公司 | Traditional Chinese medicine prescription recommendation sorting method based on data matching |
| CN111816305A (en) * | 2020-08-05 | 2020-10-23 | 沈国忠 | Cold Chinese patent medicine recommendation method based on artificial intelligence |
| CN112185503A (en) * | 2020-09-14 | 2021-01-05 | 深圳金草健康科技有限公司 | Intelligent auxiliary diagnosis system and method for traditional Chinese medicine |
| CN112420191A (en) * | 2020-11-23 | 2021-02-26 | 北京麦岐科技有限责任公司 | Traditional Chinese medicine auxiliary decision making system and method |
| CN113241173A (en) * | 2021-05-12 | 2021-08-10 | 华中科技大学 | Traditional Chinese medicine auxiliary diagnosis and treatment method and system for chronic obstructive pulmonary disease |
| CN114628001A (en) * | 2022-03-16 | 2022-06-14 | 平安科技（深圳）有限公司 | Prescription recommendation method, system, equipment and storage medium based on neural network |
| CN115424696A (en) * | 2022-11-04 | 2022-12-02 | 之江实验室 | Traditional Chinese medicine rare disease traditional Chinese medicine prescription generation method and system based on transfer learning |
| CN115424696B (en) * | 2022-11-04 | 2023-02-03 | 之江实验室 | Traditional Chinese medicine rare disease traditional Chinese medicine prescription generation method and system based on transfer learning |
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