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

采用临床实验室检测方法对肝病决策支持系统进行了研究。该系统由两个步骤组成。采用人工神经网络和医学规则库，分别设计了反向传播神经网络对慢性非活动性肝炎、慢性活动性肝炎、肝硬化、肝细胞癌、脂肪肝、酒精性肝病、正常人等7类慢性肝病进行诊断。将神经网络的输出输入到第二步进行最终诊断。输入数据为187例肝脏实验室数据26项。系统的诊断准确率为75.4%(141 / 187)，高于5名病理医师的诊断准确率（63.0%）。该系统作为慢性肝病的决策支持系统似乎是有效的。用模糊理论诊断肝病，灵敏度高，特异性低。接下来，我们尝试用人工神经网络和决策树逻辑从30组实验室数据中对炎性疾病、肌肉或心肌疾病、贫血、恶性肿瘤、肾尿疾病、肝胆疾病、糖尿病、胃肠疾病、骨病和高脂血症等10个疾病组进行分类。10种疾病自动分类，诊断准确率为80.8%。该系统将有助于从基本的实验室数据推断出疾病群体。综上所述，神经网络对具有参数重叠限制的群体具有很强的区分能力。

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.