PubMed · 12931482
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Bernadine Healy. 2003-08-11. Whose breasts, anyway?. https://pubmed.ncbi.nlm.nih.gov/12931482/
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This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.
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The practice of medicine links diagnosis to treatment. However, for many diagnosed conditions diagnosis and treatment may not affect health outcome. Examples include low-grade cancers that do not influence life expectancy or quality of life. Further, there is considerable uncertainty about the point along a biologic continuum where treatment should begin. Changes in diagnostic thresholds often increase healthcare costs even though the benefit of the treatment is uncertain. Although this uncertainty is understood by the healthcare providers, it often is not shared with patients. We advocate a new paradigm called shared medical decision making that makes uncertainty about diagnosis and treatment transparent to patients and engages them in the treatment decision process.