Heathfield H A, Winstanley G, Kirkham N
Information Technology Research Institute, Brighton Polytechnic, Moulescoomb, East Sussex, UK.
J Biomed Eng. 1991 Jan;13(1):51-7. doi: 10.1016/0141-5425(91)90044-8.
The histopathological diagnosis of breast disease is representative of many problems of differential diagnosis encountered in the medical domain. It requires highly trained and experienced experts and is characterized by a large number of features whose presence or absence involves much uncertainty. Computer-based decision support systems intended to function in a consultative capacity during differential diagnosis have had limited success for two fundamental reasons. Firstly, they take an autonomous role and assume that the user has no contribution to make to the problem-solving process. Secondly, the established techniques for representing and reasoning with medical knowledge are of limited suitability in this domain. Such systems are unable to reach a correct diagnosis quickly and often subject the user to a cumbersome dialogue. These are not tolerated by pathologists working under severe time constraints. We first look at the problem-solving methods employed by pathologists in this domain and examine the functionality of traditional expert system methodologies. We then present a cooperative design which allows the pathologists to express his or her ideas within a decision support system whilst gaining assistance in required areas. A novel inference technique based upon the set partitioning technique in hypergraphs is also described. This mathematical method has the ability to cope with the incomplete or inadequate knowledge which is a characteristic of breast disease, whilst directing data gathering in a meaningful manner. In particular this approach can significantly reduce the amount of irrelevant data which the pathologist must enter before a conclusion is reached. Thus it can potentially improve the efficiency and user acceptability of medical expert systems.
乳腺疾病的组织病理学诊断代表了医学领域中遇到的许多鉴别诊断问题。它需要训练有素且经验丰富的专家，其特点是存在大量特征，这些特征的有无涉及很大的不确定性。旨在在鉴别诊断过程中发挥咨询作用的基于计算机的决策支持系统由于两个基本原因取得的成功有限。首先，它们扮演自主角色，假定用户对问题解决过程没有贡献。其次，用于表示医学知识和进行推理的既定技术在该领域适用性有限。这样的系统无法快速得出正确诊断，并且常常使用户陷入繁琐的对话。在严格的时间限制下工作的病理学家无法容忍这些情况。我们首先研究病理学家在该领域采用的问题解决方法，并考察传统专家系统方法的功能。然后我们提出一种协作设计，使病理学家能够在决策支持系统中表达自己的想法，同时在所需领域获得帮助。还描述了一种基于超图中集合划分技术的新颖推理技术。这种数学方法能够处理乳腺疾病所特有的不完整或不充分的知识，同时以有意义的方式指导数据收集。特别是，这种方法可以显著减少病理学家在得出结论之前必须输入的无关数据量。因此，它有可能提高医学专家系统的效率和用户接受度。
