SPHINX — A System for Computer-Aided Diagnosis

A practical review and taxonomy of fuzzy expert systems: methods and applications

Purpose

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

Design/methodology/approach

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

Findings

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Originality/value

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

A database medical diagnostic support system using standardized medical data: a pilot study

In the past two decades a wide variety of information systems have been developed to facilitate medical diagnosis, better known as medical expert systems. These systems can be divided into several methodological categories, one of which is the database approach. A problem with this approach is the difficulty in filling the knowledge base directly from the source text because of complexities of natural language. This paper discusses the development of a computer-aided medical di agnostic system using standardized symptom codes as its input. based upon the results of a comprehensive questionnaire that is filled out by the patient. Subsequently, the patient's potential disease is predicted by linking symptoms with most likely diseases. The methodology of the system is discussed and a few examples are presented, as well as directions for further devel opment of this approach to medical information processing and decision making.

Assessment of a scheme for prioritizing inorganic toxicants by using signs-and-symptoms analysis

In cases of exposure to unknown substances, analyzing environmental or biological samples for elevated levels of toxicants can be extremely complex. We assessed a method for prioritizing toxic substances according to the agreement between toxic-specific and incident-reported signs and symptoms by analyzing 25 reported case histories of exposure to inorganic toxicants. In all but one case, this analysis scheme successfully prioritized toxicants for subsequent analysis. Despite its limitations, the scheme is a substantial improvement over other available methods.