Chard T
Department of Obstetrics, St. Bartholomew's Hospital Medical College, London, UK.
Med Decis Making. 1991 Jan-Mar;11(1):38-41. doi: 10.1177/0272989X9101100106.
The use of Bayes' theorem as a diagnostic tool in clinical medicine normally requires an input of exact probability estimates. However, humans tend to think in categories ("likely," "unlikely," etc.) rather than in terms of exact probability. A computer simulation of the presenting features of a case of pelvic infection has been used to compare the effects of quantitative and qualitative probability estimates on the diagnostic accuracy of Bayes' theorem. For the commoner conditions (prior probability greater than or equal to 0.2) the use of a two- or three-category system is virtually equivalent to the use of exact probability. However, uncommon conditions (prior probability less than or equal to 0.03) are completely ignored by the qualitative system. It is concluded that the use of simple categories of probability is acceptable for a Bayesian diagnostic system provided that the target conditions have a relatively high prior probability.
在临床医学中，将贝叶斯定理用作诊断工具通常需要输入精确的概率估计值。然而，人类倾向于用类别（“可能”“不太可能”等）来思考，而非精确概率。利用计算机模拟盆腔感染病例的呈现特征，比较了定量和定性概率估计对贝叶斯定理诊断准确性的影响。对于较常见的病症（先验概率大于或等于0.2），使用两类或三类系统实际上等同于使用精确概率。然而，定性系统完全忽略了不常见的病症（先验概率小于或等于0.03）。得出的结论是，对于贝叶斯诊断系统而言，只要目标病症具有相对较高的先验概率，使用简单的概率类别是可以接受的。
