Fuzzy Set Theory in Medicine

Fuzzy set theory has a number of properties that make it suitable for formalizing the uncertain information upon which medical diagnosis and treatment is usually based. Firstly, it allows us to define inexact medical entities as fuzzy sets. Secondly, it provides a linguistic approach with an excellent approximation to texts. Finally, fuzzy logic offers powerful reasoning methods capable of drawing approximate inferences. These facts suggest that fuzzy set theory might be a suitable basis for the development of a computerized diagnosis and treatment-recommendation system. This is borne out by trials performed with the medical expert system CADIAG-2, which uses fuzzy set theory to formalize medical relationships

Consistency checking of binary categorical relationship in a medical knowledge base.

Knowledge bases of medical expert systems have grown to such an extent that formal methods to verify their consistency seem highly desirable; otherwise, decision results of such expert systems are not reliable and contradictory entries in the knowledge base may cause erroneous conclusions. This paper presents a new formalization of the finding/finding, finding/disease, and disease/disease relationships of the medical expert system CADIAG-1. This formalization also helps to clarify the differences between the application of propesitional logic and of quantificational logic to capture the meaning of some fundamental categorical relationships in the area of medical diagnostics. Moreover, this formalization leads to very simple yet provably correct and complete algorithms to check the consistency of a medical knowledge base containing a set of these relationships

Bilattice CADIAG-II: Theory and Experimental Results

CADIAG-II is a functioning experimental fuzzy expert system for computer-assisted differential diagnosis in internal medicine. To overcome the current limitations of the system, we propose an extension based on bilattices. The proposed changes were implemented and reviewed in a retrospective evaluation of 3,131 patients with extended information about patient’s medical history, physical examination, laboratory test results, clinical investigations and—last but not least—clinically confirmed discharge diagnoses

Fuzzy Medical Diagnosis

: Despite all standardization efforts, medical diagnosis is still considered an art. Much of this status is owed to the fact that medical diagnosis requires a proficiency in coping with uncertainty simply not found in today's computing machinery. Offering a generic and powerful framework for the remodelling of existing systems and the paradigms they are based on, fuzzy set theory promises to be a valuable contribution to the advancement of medical computing,, particularly as implementations begin to proliferate. With our work we provide an overview of fuzzy medical systems covering three different aspects of diagnosis: scoring, consultation, and diagnostic monitoring. Keywords: problems of medical diagnosis, medical scores, consultation systems, diagnostic monitoring 1. Introduction Medical diagnosis is the art of determining a person's pathological status from an available set of findings. Why is it an art? Because it is a problem complicated by many and manifold factors, an..

Temporal representation and reasoning in medicine: Research directions and challenges

OBJECTIVE: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research