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A Study on decision-support system of clinical laboratory tests.

临床检验决策支持系统研究。

基本信息

批准号：
05454601
负责人：
YAMAUCHI Kazunobu
金额：
$ 3.78万
依托单位：
Nagoya University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1995
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-05454601/
关键词：
neural metwork expert system in then rule Fuzzy theory hepatic disease decision-tree logic 心電図自動診断ルーチン検査ファジー理論

项目摘要

A study on decision-support system for liver disease using clinical laboratory tests was performed. The system was composed of two steps. One is a step to use an artificial neural network and the other is a step to use medical rule base, In the former, a back propagation neural network was designed to diagnose seven categories of chronic liver diseases : chronic inactive hepatitis, chronic active hepatitis, liver cirrhosis, hepatocellular carcinoma, fatty liver, alcoholic liver desease, and normal. The output of a neural network were input to the second step for a final diagnosis. Input data to the network were 26 items of hepatic laboratory data from 187 cases. The diagnostic accuracy of the system was 75.4% (141 of 187 cases) which was higher than the diagnostic sccuracy (63.0%) of 5 hapatologists. Yhis system was seemed to be effective as a decision-support system for chronic liver disease. When Fuzzy theory was used to diagnose liver disease, high sensitivity was obtained with low specificity.Next, we tried categorization of ten disease groups of inflammatory disease, muscular or myocardial disease, anemia, malignant tumor, reno-urinary disease, hepatobiliary disease, diabetes mellitus, gastrointestinal disease, bone disease and hyperlipidemia from 30 sets of leboratory data using artificial neural network and dicision-tree logic. Ten-disease groups were automatically classified with 80.8% of diagnostic accuracy.This system will be helpful to infer the disease groups from basic laboratory data. In conclusion, neural network in very powerful to differentiate groups which shows overlapped limits of parameters.

对临床实验室检测肝病决策支持系统进行了研究。该系统由两个步骤组成。第一步是利用人工神经网络，第二步是利用医学规则库。在前者中，设计了一个反向传播神经网络来诊断七类慢性肝病：慢性非活动性肝炎、慢性活动性肝炎、肝硬变、肝细胞癌、脂肪肝、酒精性肝病和正常。将神经网络的输出输入到第二步进行最终诊断。网络录入的数据为187例26项肝脏实验室数据。该系统的诊断正确率为75.4%(141/187)，高于5位血液科医生的诊断正确率(63.0%)。该系统作为慢性肝病的决策支持系统似乎是有效的。然后利用人工神经网络和决策树逻辑，从30组诊断数据中尝试对炎症性疾病、肌肉或心肌疾病、贫血、恶性肿瘤、肾泌尿系统疾病、肝胆疾病、糖尿病、胃肠道疾病、骨病、高脂血症等10个病种进行分类。对10个疾病组进行了自动分类，诊断准确率为80.8%。该系统将有助于从基础实验室数据推断疾病组。综上所述，神经网络在区分参数范围重叠的群体方面具有很强的能力。