Yana K, Mizuta H, Kawachi K, Yoshida H, Iida K, Okubo Y, Tohru M, Okuyama F
Department of Electronic Informatics, Hosei University, Tokyo, Japan.
Methods Inf Med. 1997 Dec;36(4-5):349-51.
This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient's first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.
伪贝叶斯分类器和神经网络分类器，它们用于根据患者首次门诊时提供的简单“是/否”问卷辅助诊断精神疾病患者。这些分类器将患者分为三种最常见的国际疾病分类（ICD）类别，即精神分裂症、情感性和神经症性障碍。使用100份完整的问卷来构建和评估分类器。伪贝叶斯分类器的平均正确决策率为73.3%，神经网络分类器的平均正确决策率为77.3%。这些比率高于有经验的精神科医生基于与分类器所使用的相同有限数据所达到的比率。这些分类器可有效地用于辅助精神科医生做出最终诊断。
