Knowledge engineering for clinical consultation programs: modeling the application area

Developers of computer-based decision-support tools frequently adopt either pattern recognition or artificial intelligence techniques as the basis for their programs. Because these developers often choose to accentuate the differences between these alternative approaches, the more fundamental similarities are frequently overlooked. The principal challenge in the creation of any clinical consultation program - regardless of the methodology that is used - lies in creating a computational model of the application domain. The difficulty in generating such a model manifests itself in symptoms that workers in the expert systems community have labeled "the knowledge-acquisition bottleneck" and "the problem of brittleness". This paper explores these two symptoms and shows how the development of consultation programs based on pattern-recognition techniques is subject to analogous difficulties. The expert systems and pattern recognition communities must recognize that they face similar challenges, and must unite to develop methods that assist with the process of building of models of complex application tasks.