CN1234092C - Predictive modelling method application to computer-aided medical diagnosis - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000003745 diagnosis Methods 0.000 title claims abstract description 23
- 238000013528 artificial neural network Methods 0.000 claims abstract description 37
- 208000024891 symptom Diseases 0.000 claims abstract description 32
- 238000012549 training Methods 0.000 claims abstract description 26
- 201000010099 disease Diseases 0.000 claims description 6
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 6
- 239000012141 concentrate Substances 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract 1
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- 230000036760 body temperature Effects 0.000 description 2
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- 238000010801 machine learning Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 238000009530 blood pressure measurement Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
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Abstract
The present invention discloses a forecast modeling method for computer-aided medical diagnosis. A medical symptom detection device acquires the symptom of objects to be diagnosed, and then a symptom vector is formed. The symptom vector is treated by a forecast model, and then the forecast result can be obtained. The forecast modeling method comprises the following steps: (1) if the forecast model is not well trained, the step (2) is executed, if the forecast model is well trained, the step (6) is proceeded; (2) an initial training data set is generated by history cases; (3) a neural network ensemble is trained by the initial training dataset; (4) the neural network ensemble processes the initial training data set, and then a rule training data set is generated; (5) a rule model is generated from the rule training data by rule learning technology in a concentration way; (6) the rule model forecasts, and gives results and explanations. The present invention has the advantage that the present invention provides a forecast modeling method with high precision and high intelligibility for computer-aided medical diagnosis devices.
Description
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 that the rule composition 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 (1)
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) but the neural network of utilizing the repeated sampling technology to generate is integrated that the initial training data set is handled with the generation rule training dataset;
(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;
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:
(4.1) with L
1Be changed to empty set;
(4.2) from initial training data set L
0In obtain a symptom vector and classification thereof;
(4.3) for each classification a counter is set respectively, is used for writing down the generic number that predicts the outcome that neural network provides;
(4.4) with all counter O resets;
(4.5) 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;
(4.6) obtain neural network integrated in k the F that predicts the outcome that neural network provides follow-up symptom vector
k
(4.7) with F
kThe counter of pairing classification adds 1;
(4.8) k is added 1;
(4.9) 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 (4.6) to; Otherwise execution in step (4.10);
(4.10) 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;
(4.11) current symptom vector and new classification thereof are added L
1
(4.12) judge L
0In whether also have the symptom vector of not investigating, if having then forward step (4.2) to; Otherwise enter step (4.13);
(4.13) finish.
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