医学诊断
判别函数分析
贝叶斯定理
样品(材料)
决策树
线性判别分析
人工智能
计算机科学
人口
朴素贝叶斯分类器
机器学习
先验与后验
数据挖掘
统计
模式识别(心理学)
数学
医学
贝叶斯概率
病理
支持向量机
哲学
认识论
化学
环境卫生
色谱法
标识
DOI:10.1001/archpsyc.1972.01750290057011
摘要
Three methods for generating psychiatric diagnoses by computer are compared on measures of agreement between computer and clinical diagnoses made on actual samples of cases. Rules for two of the methods, Bayes and discriminant function classification, were derived from characteristics of subjects in a developmental sample. Rules for the third method, DIAGNO II—an example of the logical decision tree approach to differential diagnosis—had been derived a priori. The three methods performed equally well on a cross-validation sample drawn from the same population as the developmental sample. DIAGNO II performed most accurately on a cross-validation sample from a new population. Reasons are given why, at the present time, a logical decision tree method such as DIAGNO II is preferable to the Bayes and discriminant function methods for computer diagnosis.
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