Improving decisionmaking processes with the fuzzy logic approach in the epidemiology of sleep disorders

Abstract

Epidemiological studies can provide information not only on specific diagnostic entities but also on their underlying symptomatic constellations. For this purpose, an expert system was developed for the assessment of sleep disorders and endowed with the fuzzy logic capabilities necessary to determine the degree to which a given symptom corresponds to a specific diagnosis. Uncertainty is inherent in fields such as sleep medicine and psychiatry, and becomes evident in clinical practice at the stages of data collection and diagnostic formulation, when the clinician must determine whether a symptom is present and must choose from several diagnostic possibilities. The process involves a considerable degree of subjectivity on the part of the patient in trying to describe his or her symptoms, and of the clinician whose final diagnosis will depend on his or her clinical experience and interpretation of what is normal and what is pathological. Inferential models of the probabilistic or fuzzy logic type take into account such uncertainty. The Sleep-Eval system has been used in epidemiological and clinical studies involving 34,044 interviews collected by close to 300 interviewers. The diagnostic potential of these models is illustrated using data collected in an epidemiological study of the noninstitutionalized general population of Italy and underlines the advantages and limits of the binary, bayesian, and fuzzy logic methods and analyses.

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