健診システムにおける「曖昧さ」の取り扱いに関するアプローチ

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We have developed an Expert System (ES) which assists the Doctors in the Automated Multiphasic Health Testing and Service (AMHTS), specially in Diabetes Mellitus. In the AMHTS the Doctors classify the patient conditions to 6 special categories (A, B, BF, C, D, G) difined in AMHTS. Almost of all ES can infer the Diagnosis only by using Rule Base with crispy threshold. These are not so sufficient for Doctors, because Doctors have ambiguous sense. We have developed ES, applied to DM, utilizing two types of methods indicating continuous certainty. One is the Index method similar to FUZZY integration theory. The other is the ES with Neural Network (NN). In the Index method, we have newly introduced a Diabetes Mellitus Index (DMI) which indicates the degree of DM. DMI is calculated by converting the 3 blood sugar (FBS, 1hGTT, 2hGTT) using non-linear scale, and multiplying the weightings corresponding to the each blood sugar value. Subsequently, inference is carried out using a rule base and DMI. Consequently, the categories from the ES upon 1000 patients data in Toshiba Rinkan Hospital have demonstrated a high degree of coincidence with the categories by the Doctors employed in the AMHTS. NN is constructed with 3 layers as the input layer, the hidden layer and the output layer. The input layer has 3 units assigned to 3 blood suger. The hidden layer has 50 units. The output layer has 5 units assigned to 5 categories except "A" category. "A" category is infered by the rule base. Consequently, the coincident ratio between the categories from the ES with NN and ones by the Doctors upon 3684 patients including above 1000 data has been sufficient. It is, however, less than the coincident ratio from the ES with DMI. Each of the 4 methods (FUZZY, Index, NN, Rule) is suitable and effective to the Medical Application, and so it is important to apply and combine the 4 methods to the appropriate problems.

健診システムにおける「曖昧さ」の取り扱いに関するアプローチ

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