CN1477581A - Predictive modelling method application to computer-aided medical diagnosis - Google Patents

Predictive modelling method application to computer-aided medical diagnosis Download PDF

Info

Publication number
CN1477581A
CN1477581A CNA031321410A CN03132141A CN1477581A CN 1477581 A CN1477581 A CN 1477581A CN A031321410 A CNA031321410 A CN A031321410A CN 03132141 A CN03132141 A CN 03132141A CN 1477581 A CN1477581 A CN 1477581A
Authority
CN
China
Prior art keywords
neural network
symptom
integrated
utilize
classification
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CNA031321410A
Other languages
Chinese (zh)
Other versions
CN1234092C (en
Inventor
周志华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University
Original Assignee
Nanjing University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University filed Critical Nanjing University
Priority to CNB031321410A priority Critical patent/CN1234092C/en
Publication of CN1477581A publication Critical patent/CN1477581A/en
Application granted granted Critical
Publication of CN1234092C publication Critical patent/CN1234092C/en
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Images

Abstract

The present invention discloses a prediction model-building method for computer-aided medical diagnosis. It utilizes the medical symptom detection equipment to obtain the symptom of object to be diagnosed to form symptomatic vector, then utilizes the prediction model to make treatment so as to can obtain prediction result. It also provides its concrete steps for implementing said invented prediction model-building method.

Description

A kind of forecast modeling method that is applicable to computer-aided medical diagnosis
One, technical field
The present invention relates to a kind of computer-aided medical diagnosis device, the particularly a kind of high precision of neural network integrated technology and rule learning technology, high intelligibility forecast modeling method utilized.
Two, background technology
Along with development of computer, the computer-aided medical diagnosis device has become important auxiliary diagnosis means owing to be not subjected to the influence of factors such as fatigue, mood.The computer-aided medical diagnosis device normally utilizes some forecast modeling methods that historical case is analyzed, thereby set up forecast model, and then come the new case is diagnosed with this forecast model, its result submits to the medical expert and further analyzes and make a definite diagnosis, thereby alleviates medical expert's work load to a certain extent.Therefore, the forecast modeling method is the key of computer-aided medical diagnosis device.On the one hand, because medical diagnosis must be accurately, therefore the forecast modeling method that is suitable for must have very high precision; On the other hand, because medical diagnosis is concerning the quilt person's of examining healthy and life security, therefore the forecast modeling method that is suitable for must have very high intelligibility, promptly after making diagnosis, also need to provide explanation to diagnosis, this is not only by the person of examining and family members' thereof needs, or the medical expert checks the needs of diagnostic procedure.Yet,, do not have high intelligibility though prior art such as neural network etc. have high precision; Though and rule learning etc. have high intelligibility, do not have high precision, this has just caused adverse effect to the performance of computer-aided medical diagnosis device.
Three, summary of the invention
The objective of the invention is to be difficult to produce the high precision that is applicable to the computer-aided medical diagnosis device, the problem of high intelligibility forecast model at prior art, provide the forecast modeling method of a kind of high precision, high intelligibility, with the auxiliary performance that improves the computer-aided medical diagnosis device.
For realizing purpose of the present invention, the invention provides a kind of method of utilizing neural network integrated technology in the machine learning and rule learning technology to carry out forecast modeling, this method may further comprise the steps: (1) if forecast model does not train, then execution in step 2, otherwise forward step 6 to; (2) utilize historical case to produce the initial training data set; (3) it is integrated to utilize the initial training data set to train a neural network; (4) utilize that neural network is integrated to be handled with the generation rule training dataset the initial training data set; (5) utilize the rule learning technology to concentrate the generation rule model from regular training data; (6) utilize rule model to predict and provide result and explanation; (7) finish.
Advantage of the present invention is the forecast modeling method that a kind of high precision, high intelligibility are provided for the computer-aided medical diagnosis device, with the auxiliary performance that improves the computer-aided medical diagnosis device.
Below in conjunction with accompanying drawing most preferred embodiment is elaborated.
Four, description of drawings
Fig. 1 is the workflow diagram of computer-aided medical diagnosis device.
Fig. 2 is the process flow diagram of the inventive method.
Fig. 3 is the process flow diagram with the integrated generation rule training dataset of neural network.
Five, embodiment
As shown in Figure 1, the computer-aided medical diagnosis device utilizes for example symptom for example body temperature, the blood pressure etc. that obtain the follow-up object such as body temperature, blood pressure measurement device of medical symptom checkout equipment, symptom is quantized to obtain symptom vector, for example [t then 1, t 2..., t n], t wherein 1Represent first symptom value, t 2Represent second symptom value, the rest may be inferred.The symptom vector is given forecast model and is handled, and the digitized representations form that can be predicted the outcome and explain after the literal processing, has just produced diagnosis and the explanation of submitting to the user at last.
Method of the present invention as shown in Figure 2.Step 10 is initial actuatings.Step 11 judges whether forecast model trains, and can handle diagnostic task, execution in step 16 if trained then; Otherwise need train execution in step 12.Step 12 utilizes historical case to produce the initial training data set, for sake of convenience, claims that the initial training data set is L 0L 0In comprised pairing symptom vector of each historical case and classification thereof, the disease specific classification of promptly diagnosing out (" not having disease " is also as a kind).But step 13 utilizes repeated sampling technology commonly used in the statistics from L 0In produce N data set, and train a neural network with concentrated each of this N data, it is integrated that these neural networks have just been formed neural network.N is the round values for example 9 of a user preset, and it has determined the integrated neural network number that comprises of neural network.Neural network used herein can be the neural network of any kind, as long as can carry out the prediction task, for example can use the multi-layer feed-forward BP network of introducing in the neural network textbook.Step 14 utilizes the integrated generation of neural network to be used to set up the regular training dataset L of rule model 1, this step will be specifically introduced in conjunction with Fig. 3 in the part of back.
The step 15 of Fig. 2 is utilized L 1Train rule model.Rule model is a forecast model of being made up of the rule of a lot of bar IF-Then or similar type, and it (is exactly L by certain rule learning method from certain training dataset here 1) in training come out.Here the rule learning method of any kind be can use,, RIPPER, the C4.5 Rule etc. that introduce in the machine learning textbook for example can be used as long as the model of its generation can be carried out the prediction task.Step 16 receives symptom vector to be diagnosed.Step 17 is submitted to the rule model that trains with the symptom vector and is predicted.Step 18 provides the rule of using in predicting the outcome of rule model generation and the forecasting process, and these rules have just been formed the explanation that this is predicted the outcome.Step 19 is done states.
Because the forecast model that method of the present invention is set up is a rule model, so it has the high property understood; Because this method has been utilized and had the integrated training dataset of setting up rule model that produces of high-precision neural network, and this can be considered as initial data set has been carried out optimum processing such as denoising, enhancing, therefore the rule model of setting up also has high precision again.
Fig. 3 describes the step 14 of Fig. 2 in detail, and its effect is to utilize the integrated regular training dataset L that is used to set up rule model that produces of neural network 1The step 140 of Fig. 3 is initial states.Step 141 is with L 1Be changed to empty set.Step 142 is from the initial training data set L of step 12 generation of Fig. 2 0In obtain a symptom vector and classification thereof.Step 143 is provided with a counter respectively for each classification, it is this classification that these counters are used for recording predicting the outcome that what neural networks provide, of all categories corresponding respectively the disease specific classification of diagnosing out (" not having disease " is also as a kind) here.Step 144 is with all counter O resets.Step 145 is changed to 1 with controlled variable k, k be one more than or equal to 1 but smaller or equal to the round values of the N of step 13 among Fig. 2, it is used to refer to the sequence number of the neural network of current investigation.Step 146 obtain neural network integrated in k neural network to predicting the outcome that follow-up symptom vector provides, for sake of convenience, claim this result for F kStep 147 is with F kThe counter of pairing classification adds one.Step 148 adds one with k.Step 149 judge k whether smaller or equal to neural network integrated in the number of neural network, promptly the N of step 13 among Fig. 2 if show that then other neural networks are investigated as yet in addition, forwards step 146 to; Otherwise with regard to execution in step 150.
Value in the step 150 of Fig. 3 pair all counters compares, the counter that the value of finding out is maximum, and with the new classification of its corresponding class as current symptom vector; If there is the value in a plurality of counters to be maximal value, then to occur the new classification of the kinds of Diseases of chance maximum in these counter corresponding class as current symptom vector.Step 151 adds L with current symptom vector and new classification thereof 1Step 152 judges among the L0 whether to also have the symptom vector of not investigating, if having then forward step 142 to; Otherwise just enter step 153, i.e. the done state of Fig. 3.

Claims (2)

1, a kind of forecast modeling method that is applicable to computer-aided medical diagnosis comprises the symptom of obtaining the follow-up object by the medical symptom checkout equipment, then symptom is quantized to obtain symptom vector [t 1, t 2..., t n], t wherein nRepresent n symptom value, the symptom vector is given forecast model and is handled, and the digitized representations form that can be predicted the outcome and explain is characterized in that this method may further comprise the steps:
(1) if forecast model does not train, execution in step (2) then, otherwise forward step (6) to;
(2) utilize historical case to produce the initial training data set;
(3) it is integrated to utilize the initial training data set to train a neural network;
(4) utilize that neural network is integrated to be handled with the generation rule training dataset the initial training data set;
(5) utilize the rule learning technology to concentrate the generation rule model from regular training data;
(6) utilize rule model to predict and provide result and explanation;
(7) finish.
2, the forecast modeling method that is applicable to computer-aided medical diagnosis according to claim 1 is characterized in that:
In (4), utilize the integrated generation of neural network to be used to set up the regular training dataset L of rule model 1Step be:
(1) with L 1Be changed to empty set;
(2) from initial training data set L 0In obtain a symptom vector and classification thereof;
(3) for each classification a counter is set respectively, is used for writing down the generic number that predicts the outcome that neural network provides;
(4) with all counter O resets;
(5) controlled variable k is changed to 1, k be one more than or equal to 1 but smaller or equal to neural network integrated in the number N of neural network;
(6) obtain neural network integrated in k the F that predicts the outcome that neural network provides follow-up symptom vector k
(7) with F kThe counter of pairing classification adds 1;
(8) k is added 1;
(9) judge k whether smaller or equal to neural network integrated in the number N of neural network, if show that then other neural networks are investigated as yet in addition, forward step (6) to; Otherwise execution in step (10);
(10) value in all counters is compared, the counter that the value of finding out is maximum, and with the new classification of its corresponding class as current symptom vector; If there is the value in a plurality of counters to be maximal value, then to occur the new classification of the kinds of Diseases of chance maximum in these counter corresponding class as current symptom vector;
(11) current symptom vector and new classification thereof are added L 1
(12) judge L 0In whether also have the symptom vector of not investigating, if having then forward step (2) to; Otherwise enter step (13);
(13) finish.
CNB031321410A 2003-07-01 2003-07-01 Predictive modelling method application to computer-aided medical diagnosis Expired - Fee Related CN1234092C (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CNB031321410A CN1234092C (en) 2003-07-01 2003-07-01 Predictive modelling method application to computer-aided medical diagnosis

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CNB031321410A CN1234092C (en) 2003-07-01 2003-07-01 Predictive modelling method application to computer-aided medical diagnosis

Publications (2)

Publication Number Publication Date
CN1477581A true CN1477581A (en) 2004-02-25
CN1234092C CN1234092C (en) 2005-12-28

Family

ID=34154002

Family Applications (1)

Application Number Title Priority Date Filing Date
CNB031321410A Expired - Fee Related CN1234092C (en) 2003-07-01 2003-07-01 Predictive modelling method application to computer-aided medical diagnosis

Country Status (1)

Country Link
CN (1) CN1234092C (en)

Cited By (64)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7769600B2 (en) 1997-03-13 2010-08-03 Clinical Decision Support Disease management system and method
US7780595B2 (en) 2003-05-15 2010-08-24 Clinical Decision Support, Llc Panel diagnostic method and system
US8015138B2 (en) 1993-12-29 2011-09-06 Clinical Decision Support, Llc Computerized medical self-diagnostic and treatment advice system
US8019582B2 (en) 2000-02-14 2011-09-13 Clinical Decision Support, Llc Automated diagnostic system and method
USRE43433E1 (en) 1993-12-29 2012-05-29 Clinical Decision Support, Llc Computerized medical diagnostic and treatment advice system
US8337409B2 (en) 1993-12-29 2012-12-25 Clinical Decision Support Llc Computerized medical diagnostic system utilizing list-based processing
US9005119B2 (en) 1993-12-29 2015-04-14 Clinical Decision Support, Llc Computerized medical diagnostic and treatment advice system including network access
US9081879B2 (en) 2004-10-22 2015-07-14 Clinical Decision Support, Llc Matrix interface for medical diagnostic and treatment advice system and method
CN106202986A (en) * 2016-09-28 2016-12-07 湖南老码信息科技有限责任公司 A kind of tonsillitis Forecasting Methodology based on increment type neural network model and prognoses system
CN106250714A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of renal calculus Forecasting Methodology based on increment type neural network model and prognoses system
CN106250715A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of chronic pharyngolaryngitis Forecasting Methodology based on increment type neural network model and prognoses system
CN106250712A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of ureteral calculus Forecasting Methodology based on increment type neural network model and prognoses system
CN106250713A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of vesical calculus Forecasting Methodology based on increment type neural network model and prognoses system
CN106250716A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of neurodermatitis Forecasting Methodology based on increment type neural network model and prognoses system
CN106295238A (en) * 2016-09-28 2017-01-04 湖南老码信息科技有限责任公司 A kind of hypertensive nephropathy Forecasting Methodology based on increment type neural network model and prognoses system
CN106295239A (en) * 2016-09-28 2017-01-04 湖南老码信息科技有限责任公司 A kind of fatty liver Forecasting Methodology based on increment type neural network model and prognoses system
CN106339605A (en) * 2016-09-28 2017-01-18 湖南老码信息科技有限责任公司 Colonitis prediction method and colonitis prediction system based on incremental nerve network model
CN106339607A (en) * 2016-09-28 2017-01-18 湖南老码信息科技有限责任公司 Rheumatism prediction method and rheumatism prediction system based on incremental nerve network model
CN106339606A (en) * 2016-09-28 2017-01-18 湖南老码信息科技有限责任公司 Alcohol liver prediction method based on incremental nerve network model and alcohol liver prediction system based on incremental nerve network model
CN106355034A (en) * 2016-09-28 2017-01-25 湖南老码信息科技有限责任公司 Sub-health prediction method and prediction system based on incremental neural network model
CN106355035A (en) * 2016-09-28 2017-01-25 湖南老码信息科技有限责任公司 Pneumonia prediction method and prediction system based on incremental neural network model
CN106361288A (en) * 2016-09-28 2017-02-01 湖南老码信息科技有限责任公司 Summer dermatitis prediction method and system based on incremental neural network model
CN106372442A (en) * 2016-09-28 2017-02-01 湖南老码信息科技有限责任公司 Dental ulcer prediction method and system based on incremental neural network model
CN106384006A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based hepatitis C prediction method and prediction system
CN106384011A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based mite dermatitis prediction method and prediction system
CN106384005A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based depression prediction method and prediction system
CN106384012A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based allergic dermatitis prediction method and prediction system
CN106384013A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based type-II diabetes prediction method and prediction system
CN106384007A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based hemorrhoids prediction method and prediction system
CN106384009A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based HIV prediction method and prediction system
CN106384008A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based allergic rhinitis prediction method and prediction system
CN106407693A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Hepatitis B prediction method and prediction system based on incremental neural network model
CN106407697A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Chronic fatigue syndrome prediction method and prediction system based on incremental neural network model
CN106407698A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Vegetative nervous disorder prediction method and prediction system based on incremental neural network model
CN106407695A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Anxiety disorder prediction method and prediction system based on incremental neural network model
CN106407694A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Neurasthenia prediction method and prediction system based on incremental neural network model
CN106407696A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Prickly heat prediction method and prediction system based on incremental neural network model
CN106407699A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Coronary heart disease prediction method and prediction system based on incremental neural network model
CN106407700A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Hyperthyroidism prediction method and prediction system based on incremental neural network model
CN106446550A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Cold prediction method and system based on incremental neutral network model
CN106446556A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for epifolliculitis based on incremental type neural network model
CN106446552A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for sleep disorder based on incremental neural network model
CN106446560A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Hyperlipidemia prediction method and prediction system based on incremental neural network model
CN106446551A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Incremental neural network model based chronic gastroenteritis prediction method and system
CN106446561A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Incremental neural network model based urticaria prediction method and system
CN106446557A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for hepatitis A based on incremental type neural network model
CN106446549A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for dyspepsia based on incremental type neural network model
CN106446563A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Incremental neural network model based constipation prediction method and system
CN106446554A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Seborrheic dermatitis prediction method and prediction system based on incremental neural network model
CN106446553A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Stomach illness prediction method and prediction system based on incremental neural network model
CN106446558A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Diabetes insipidus prediction method based on incremental neural network model and prediction system
CN106446559A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for viral dermatitis based on incremental type neural network model
CN106446562A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Gastric concretion prediction method and prediction system based on incremental neural network model
CN106485065A (en) * 2016-09-28 2017-03-08 湖南老码信息科技有限责任公司 A kind of chronic bronchitis Forecasting Methodology based on increment type neural network model and forecasting system
CN106650206A (en) * 2016-09-28 2017-05-10 湖南老码信息科技有限责任公司 Prediction method of high blood pressure based on incremental neural network model and prediction system
CN106777966A (en) * 2016-12-13 2017-05-31 天津迈沃医药技术股份有限公司 Data interactive training method and system based on medical information platform
CN106934223A (en) * 2017-02-28 2017-07-07 思派（北京）网络科技有限公司 A kind of blood disease intelligent classification system and method based on big data
CN106951917A (en) * 2017-02-28 2017-07-14 思派（北京）网络科技有限公司 The intelligent classification system and method for a kind of lymthoma histological type
CN107103182A (en) * 2017-03-28 2017-08-29 南京医科大学 A kind of heart disease Warning System and method based on deep learning algorithm
CN107403061A (en) * 2017-07-07 2017-11-28 中北大学 User's medical assessment model building method and medical assessment server
CN107705828A (en) * 2017-09-20 2018-02-16 广西金域医学检验所有限公司 Prejudge detection and processing method and processing device, terminal device, the storage medium of rule
CN110009007A (en) * 2019-03-18 2019-07-12 武汉大学 A kind of artificial intelligence surgical assistant system towards polymorphic type disease
CN110164544A (en) * 2018-02-11 2019-08-23 深圳欧德蒙科技有限公司 A kind of method, apparatus and terminal device of illness information processing
CN113707326A (en) * 2021-10-27 2021-11-26 深圳迈瑞软件技术有限公司 Clinical early warning method, early warning system and storage medium

Cited By (80)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
USRE43433E1 (en) 1993-12-29 2012-05-29 Clinical Decision Support, Llc Computerized medical diagnostic and treatment advice system
US9005119B2 (en) 1993-12-29 2015-04-14 Clinical Decision Support, Llc Computerized medical diagnostic and treatment advice system including network access
US8337409B2 (en) 1993-12-29 2012-12-25 Clinical Decision Support Llc Computerized medical diagnostic system utilizing list-based processing
US8015138B2 (en) 1993-12-29 2011-09-06 Clinical Decision Support, Llc Computerized medical self-diagnostic and treatment advice system
USRE43548E1 (en) 1993-12-29 2012-07-24 Clinical Decision Support, Llc Computerized medical diagnostic and treatment advice system
US8392217B2 (en) 1997-03-13 2013-03-05 Clinical Decision Support, Llc Disease management system and method including preview mode
US8060378B2 (en) 1997-03-13 2011-11-15 Clinical Decision Support, Llc Disease management system and method including question version
US8066636B2 (en) 1997-03-13 2011-11-29 Clinical Decision Support, Llc Disease management system and method including pain code
US7769600B2 (en) 1997-03-13 2010-08-03 Clinical Decision Support Disease management system and method
US8740790B2 (en) 1997-03-13 2014-06-03 Clinical Decision Support, Llc Disease management system and method
US7993267B2 (en) 1997-03-13 2011-08-09 Clinical Decision Support, Llc Disease management system including a no response method
US8727979B2 (en) 1997-03-13 2014-05-20 Clinical Decision Support, Llc Disease management system and method including significant symptom filtering
US8628470B1 (en) 1997-03-13 2014-01-14 Clinical Decision Support, Llc Disease management system and method including medication therapy self-management
US8630875B2 (en) 1997-03-13 2014-01-14 Clinical Decision Support, Llc Disease management system and health assessment method
US8663104B2 (en) 1997-03-13 2014-03-04 Clinical Decision Support, Llc Disease management system and method including therapeutic alterations permission level
US8682694B2 (en) 1997-03-13 2014-03-25 Clinical Decision Support, Llc Disease management system and method including permission database
US8727976B2 (en) 1997-03-13 2014-05-20 Clinical Decision Support, Llc Disease management system operating on a network
US8019582B2 (en) 2000-02-14 2011-09-13 Clinical Decision Support, Llc Automated diagnostic system and method
US8055516B2 (en) 2003-05-15 2011-11-08 Clinical Decision Support, Llc Panel diagnostic method and system
US8731968B1 (en) 2003-05-15 2014-05-20 Clinical Decision Support, Llc Panel diagnostic method and system including automated diagnostic analysis
US8301467B2 (en) 2003-05-15 2012-10-30 Clinical Decision Support, Llc Panel diagnostic method and system including active and passive strategies
US7780595B2 (en) 2003-05-15 2010-08-24 Clinical Decision Support, Llc Panel diagnostic method and system
US9081879B2 (en) 2004-10-22 2015-07-14 Clinical Decision Support, Llc Matrix interface for medical diagnostic and treatment advice system and method
CN106384005A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based depression prediction method and prediction system
CN106407699A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Coronary heart disease prediction method and prediction system based on incremental neural network model
CN106250715A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of chronic pharyngolaryngitis Forecasting Methodology based on increment type neural network model and prognoses system
CN106250712A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of ureteral calculus Forecasting Methodology based on increment type neural network model and prognoses system
CN106250713A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of vesical calculus Forecasting Methodology based on increment type neural network model and prognoses system
CN106250716A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of neurodermatitis Forecasting Methodology based on increment type neural network model and prognoses system
CN106295238A (en) * 2016-09-28 2017-01-04 湖南老码信息科技有限责任公司 A kind of hypertensive nephropathy Forecasting Methodology based on increment type neural network model and prognoses system
CN106295239A (en) * 2016-09-28 2017-01-04 湖南老码信息科技有限责任公司 A kind of fatty liver Forecasting Methodology based on increment type neural network model and prognoses system
CN106339605A (en) * 2016-09-28 2017-01-18 湖南老码信息科技有限责任公司 Colonitis prediction method and colonitis prediction system based on incremental nerve network model
CN106339607A (en) * 2016-09-28 2017-01-18 湖南老码信息科技有限责任公司 Rheumatism prediction method and rheumatism prediction system based on incremental nerve network model
CN106339606A (en) * 2016-09-28 2017-01-18 湖南老码信息科技有限责任公司 Alcohol liver prediction method based on incremental nerve network model and alcohol liver prediction system based on incremental nerve network model
CN106355034A (en) * 2016-09-28 2017-01-25 湖南老码信息科技有限责任公司 Sub-health prediction method and prediction system based on incremental neural network model
CN106355035A (en) * 2016-09-28 2017-01-25 湖南老码信息科技有限责任公司 Pneumonia prediction method and prediction system based on incremental neural network model
CN106361288A (en) * 2016-09-28 2017-02-01 湖南老码信息科技有限责任公司 Summer dermatitis prediction method and system based on incremental neural network model
CN106372442A (en) * 2016-09-28 2017-02-01 湖南老码信息科技有限责任公司 Dental ulcer prediction method and system based on incremental neural network model
CN106384006A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based hepatitis C prediction method and prediction system
CN106384011A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based mite dermatitis prediction method and prediction system
CN106202986A (en) * 2016-09-28 2016-12-07 湖南老码信息科技有限责任公司 A kind of tonsillitis Forecasting Methodology based on increment type neural network model and prognoses system
CN106384012A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based allergic dermatitis prediction method and prediction system
CN106384013A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based type-II diabetes prediction method and prediction system
CN106384007A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based hemorrhoids prediction method and prediction system
CN106384009A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based HIV prediction method and prediction system
CN106384008A (en) * 2016-09-28 2017-02-08 湖南老码信息科技有限责任公司 Incremental neural network model-based allergic rhinitis prediction method and prediction system
CN106407693A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Hepatitis B prediction method and prediction system based on incremental neural network model
CN106407697A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Chronic fatigue syndrome prediction method and prediction system based on incremental neural network model
CN106407698A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Vegetative nervous disorder prediction method and prediction system based on incremental neural network model
CN106407695A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Anxiety disorder prediction method and prediction system based on incremental neural network model
CN106407694A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Neurasthenia prediction method and prediction system based on incremental neural network model
CN106407696A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Prickly heat prediction method and prediction system based on incremental neural network model
CN106250714A (en) * 2016-09-28 2016-12-21 湖南老码信息科技有限责任公司 A kind of renal calculus Forecasting Methodology based on increment type neural network model and prognoses system
CN106407700A (en) * 2016-09-28 2017-02-15 湖南老码信息科技有限责任公司 Hyperthyroidism prediction method and prediction system based on incremental neural network model
CN106446550A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Cold prediction method and system based on incremental neutral network model
CN106446556A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for epifolliculitis based on incremental type neural network model
CN106446552A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for sleep disorder based on incremental neural network model
CN106446560A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Hyperlipidemia prediction method and prediction system based on incremental neural network model
CN106446551A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Incremental neural network model based chronic gastroenteritis prediction method and system
CN106446561A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Incremental neural network model based urticaria prediction method and system
CN106446557A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for hepatitis A based on incremental type neural network model
CN106446549A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for dyspepsia based on incremental type neural network model
CN106446563A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Incremental neural network model based constipation prediction method and system
CN106446554A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Seborrheic dermatitis prediction method and prediction system based on incremental neural network model
CN106446553A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Stomach illness prediction method and prediction system based on incremental neural network model
CN106446558A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Diabetes insipidus prediction method based on incremental neural network model and prediction system
CN106446559A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Prediction method and prediction system for viral dermatitis based on incremental type neural network model
CN106446562A (en) * 2016-09-28 2017-02-22 湖南老码信息科技有限责任公司 Gastric concretion prediction method and prediction system based on incremental neural network model
CN106485065A (en) * 2016-09-28 2017-03-08 湖南老码信息科技有限责任公司 A kind of chronic bronchitis Forecasting Methodology based on increment type neural network model and forecasting system
CN106650206A (en) * 2016-09-28 2017-05-10 湖南老码信息科技有限责任公司 Prediction method of high blood pressure based on incremental neural network model and prediction system
CN106777966A (en) * 2016-12-13 2017-05-31 天津迈沃医药技术股份有限公司 Data interactive training method and system based on medical information platform
CN106777966B (en) * 2016-12-13 2020-02-07 天津迈沃医药技术股份有限公司 Data interactive training method and system based on medical information platform
CN106934223A (en) * 2017-02-28 2017-07-07 思派（北京）网络科技有限公司 A kind of blood disease intelligent classification system and method based on big data
CN106951917A (en) * 2017-02-28 2017-07-14 思派（北京）网络科技有限公司 The intelligent classification system and method for a kind of lymthoma histological type
CN107103182A (en) * 2017-03-28 2017-08-29 南京医科大学 A kind of heart disease Warning System and method based on deep learning algorithm
CN107403061A (en) * 2017-07-07 2017-11-28 中北大学 User's medical assessment model building method and medical assessment server
CN107705828A (en) * 2017-09-20 2018-02-16 广西金域医学检验所有限公司 Prejudge detection and processing method and processing device, terminal device, the storage medium of rule
CN110164544A (en) * 2018-02-11 2019-08-23 深圳欧德蒙科技有限公司 A kind of method, apparatus and terminal device of illness information processing
CN110009007A (en) * 2019-03-18 2019-07-12 武汉大学 A kind of artificial intelligence surgical assistant system towards polymorphic type disease
CN113707326A (en) * 2021-10-27 2021-11-26 深圳迈瑞软件技术有限公司 Clinical early warning method, early warning system and storage medium

Also Published As

Publication number Publication date
CN1234092C (en) 2005-12-28

Similar Documents

Publication Publication Date Title
CN1234092C (en) Predictive modelling method application to computer-aided medical diagnosis
Webb The impact of artificial intelligence on the labor market
Liu et al. Evolutionary optimization of software quality modeling with multiple repositories
CN110334843B (en) Time-varying attention improved Bi-LSTM hospitalization and hospitalization behavior prediction method and device
Stern Neural networks in applied statistics
JP2821189B2 (en) Learning type decision support system
ȚĂRANU Data mining in healthcare: decision making and precision
US8015136B1 (en) Algorithmic method for generating a medical utilization profile for a patient and to be used for medical risk analysis decisioning
Chen et al. Artificial neural networks to classify mean shifts from multivariate χ2 chart signals
CN113837356B (en) Intelligent sewage treatment prediction method based on fused neural network
EP0468229A2 (en) A neural network with expert system functionality
Cheng et al. Using neural networks to detect the bivariate process variance shifts pattern
CN110310740A (en) Based on see a doctor again information forecasting method and the system for intersecting attention neural network
Han Discriminating risky software project using neural networks
Lemke et al. Medical data analysis using self-organizing data mining technologies
Khoei et al. A stacking-based ensemble learning model with genetic algorithm for detecting early stages of Alzheimer’s disease
CN1760881A (en) Modeling method of forecast in device of computer aided diagnosis through using not diagnosed cases
Savchenko et al. Metamodeling as a way to universalization of inductive modeling tools
Walia et al. A survey on applications of adaptive neuro fuzzy inference system
Ying et al. Self-learning fuzzy automaton with input and output fuzzy sets for system modelling
Abitha et al. Evolutionary multi-objective optimization of artificial neural network for classification of autism spectrum disorder screening
Neetha et al. Borderline-DEMNET for Multi-Class Alzheimer's Disease Classification
Sachnev An efficient classification scheme for ADHD problem based on Binary Coded Genetic Algorithm and McFIS
Nayak et al. Cognitive Computing in Software evaluation
CN113539517A (en) Prediction method of time sequence intervention effect

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
C17 Cessation of patent right
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20051228