Fuzziness in medical measurement and approximate reasoning

Abstract

Fuzziness in measurement and approximate reasoning is presented as an alternative for dealing with the vague, conflicting, and not definitive decisions in medicine and health care. Conditional restrictions work as a fuzziness mechanism of measure concerning the possibility an evidence can occur. Represented as fuzzy sets, they are elastic restrictions associated to information that is simultaneously imprecise and uncertain. The human reasoning able to capture the subjectivity, vagueness, and inexact information is accomplished by fuzzy logic. The difference and similarities among fuzzy measure and fuzziness in measure are presented, demonstrating how important these approaches assume in medicine and health care. Being a feasible structure for emulating the human reasoning, fuzzy systems are natural mechanisms for helping in deciding how to obtain a safer, more effective, more efficient, higher quality, and lower costs in medical and healthcare risk analysis, assessment, analysis, classification, decision, diagnosis, and therapeutic conduct. © Springer-Verlag Berlin Heidelberg 2013.