Fuzzy logic and semiotic methods in modeling of medical concepts

摘要

The field of medicine is a quickly growing area of application for computer-based systems. However, the use of computerized methods in this knowledge-intensive and expert-based discipline brings multiple challenges. The major problem is the modeling, representing, and interpreting of diverse medical concepts. For example, some symptoms and their etiologies are described in terms of molecular biology and genetics, physiological processes are defined using models from chemistry and physics; yet mental disorders are defined in more subjective terms of feelings, behaviours, habits, and life events. Thus, the representation of medical concepts must be sufficiently expressive to model concepts which are inherently complex, context-dependent, evolving, and often imprecise. Furthermore, the representation must be formal or, at least, sufficiently rigorous in order to be processed by computers and at the same time, the representation must be human-readable in order to be validated by humans. In this paper, we describe the modeling process of medical concepts as a mapping from the real-world medical concepts into their computational models, and further into their physical implementation. First, we define the notion of a concept as a fundamental unit of knowledge and specify the fundamental principles of the computational representation of a concept. Second, we describe the characteristics of medical concepts, specifically their historical and cultural changeability, their social and cultural ambiguity, and their varied levels of precision. Third, we present a meta-modeling framework for computational representation of medical concepts. Our framework is based on fuzzy logic and semiotic methods which allow us to explicitly model two important characteristics of medical concepts: imprecision and context-dependency. We present the framework using an example of a mental disorder, specifically, the concept of clinical depression. To exemplify the changeable and evolutionary character of medical concepts, we discuss the development of the diagnostic criteria for depression. Finally, we use the example of the assessment of depression to describe the computational representation for polythetic and multi-dimensional concepts and for categorical and non-categorical concepts. We demonstrate how the proposed modeling framework utilizes (1) a fuzzy-logic approach to represent the non-categorical (continuous) nature of the symptoms and (2) a semiotic approach to represent the polythetic (contextual interpretation) and dimensional nature of the symptoms.