Diamond L W, Mishka V G, Seal A H, Nguyen D T
Pathology Institute, University of Cologne, Germany.
J Am Med Inform Assoc. 1995 Mar-Apr;2(2):85-93. doi: 10.1136/jamia.1995.95261910.
Conceptual models for diagnostic reasoning proposed in the medical literature are presented to stimulate discussion about the issue of the appropriateness of probabilistic knowledge-based systems for medical diagnosis. Evidence is presented to corroborate the authors' view that diagnosis is a problem-solving task, rather than a decision-making task. In the authors' opinion, probabilistic reasoning is better suited to situations dealing with choices for clinical intervention, rather than to those dealing with determining the correct diagnosis. A critique is given of a diagnostic Bayesian expert system for lymph node pathology. In empirical studies, diagnostic Bayesian systems have been shown to typically list the correct diagnosis as the program's first choice 60% to 70% of the time. One reason for this undistinguished level of diagnostic performance is that Bayesian systems are not designed to represent and use knowledge the same way that an expert does.
医学文献中提出的诊断推理概念模型，旨在激发关于基于概率知识的系统用于医学诊断是否合适这一问题的讨论。文中给出证据以证实作者的观点，即诊断是一个解决问题的任务，而非决策任务。作者认为，概率推理更适用于处理临床干预选择的情况，而非确定正确诊断的情况。文中对一个用于淋巴结病理学的诊断贝叶斯专家系统进行了批判。在实证研究中，诊断贝叶斯系统通常被证明在60%到70%的时间里将正确诊断列为程序的首选。诊断性能处于这种普通水平的一个原因是，贝叶斯系统的设计方式与专家表示和使用知识的方式不同。
