Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to two-valued logic. Moreover, there are few if any published reports of expert systems in real-world use that reason about uncertainty using fuzzy logic. It appears that the limitations of fuzzy logic have not been detrimental in control applications because current fuzzy controllers are far simpler than other knowledge-based systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems. 1

: Applying the methods of Artificial Intelligence to clinical monitoring requires some kind of signal-to-symbol conversion as a prior step. Subsequent processing of the derived symbolic information must also be sensitive to history and development, as the failure to address temporal relationships between findings invariably leads to inferior results. DIAMON-1, a framework for the design of diagnostic monitors, provides two methods for the interpretation of time-varying data: one for the detection of trends based on classes of courses, and one for the tracking of disease histories modelled through deterministic automata. Both methods make use of fuzzy set theory, taking account of the elasticity of medical categories and allowing discrete disease models to mirror the patient's continuous progression through the stages of illness. Keywords: diagnostic monitoring, trend detection, disease tracking, fuzzy sets, automata 1. Introduction It is a widely appreciated fact that much...

Objective: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semiautomatic learning methods can be used to support the domain specialists. They are usually not only interested in the accuracy of the learned knowledge: the understandability and interpretability of the learned models is of prime importance as well. Then, often simple models are more favorable than complex ones. Methods and Material: We propose diagnostic scores as a promising approach for the representation of simple diagnostic knowledge, and present a method for inductive learning of diagnostic scores. It can be incrementally refined by including background knowledge. We present complexity measures for determining the complexity of the learned scores. Results: We give an evaluation of the presented approach using a case base from the fielded system SONOCONSULT. We further discuss that the user can easily balance between accuracy and complexity of the learned knowledge applying the presented measures. Conclusions: We argue that semi-automatic learning methods can support the domain specialist efficiently when building (diagnostic) knowledge systems from scratch. The presented complexity measures allow for an intuitive assessment of the learned patterns. Key words: diagnostic scores, knowledge discovery, complexity measures, data mining 1

The satisfiability problem for monadic infinite-valued Gödel logic is known to be undecidable. We identify a fragment of this logic extended with strong negation whose satisfiability is not only decidable but it is decidable within classical logic. We use this fragment to formalize the rules of CADIAG-2, a well performing fuzzy expert system assisting in the differential diagnosis in internal medicine. A (classical) satisfiability check of the resulting formulas allowed the detection of some errors in the rules of the system. 1.

In the near future, every medical information system wiil be equipped with a function that provides knowledge-based decision support. Data-driven knowledgebased decision support consists, in the first place, of a medical data-to-concept conversion step and, second, a knowledge base containing medical relationships from medical concepts to decisions. Both, concept and relationship modeling medicine can be done by using fuzzy sets, fuzzy relationships, and fuzzy decision making algorithms to conserve the inherent fuzziness of medical concepts and medical relationships. Examples of the application of type-n hzzy sets to model medical concepts and of fuzzy logic, fuzzy decision graphs, fuzzy control, and fuzzy automata in medical diagnosis, interpretative analysis of test results, device control, and data monitoring are given in the present study. Keywords: Knowledge-Based Methodology,

Abstract — We contrast the concept underlying t-norm-based propositional fuzzy logics with the problem to whose solution fuzzy logics are frequently suggested as helpful – namely, to find a model of reasoning with vague information. We argue that fuzzy logics are useful as long as truth values can be identified with the meaning of the considered propositions. This, however, is rarely the case in practice; hence we see the need to broaden the concept underlying this important class of logics and try fresh approaches. In particular, we should flexibilise the formalism to allow that propositions do not arise in the same context, but are just known to be related in some way. We tackle the problem tentatively. We define a set of rules which, as we assume, are minimally required to enable us to argue about vague propositions whose content is not taken into account. Our choice of rules reflects the practical requirements of a certain expert system on

CADIAG-2 is a medical expert system to assist differential diagnosis in several sub-specialties of internal medicine. A patient’s symptoms, signs, laboratory test results, and various clinical findings constitute the starting point of the computer-assisted differential diagnostic process. Lists of confirmed and excluded diagnoses as well as diagnostic hypotheses are the output. In this paper we logically verify CADIAG-2’s knowledge base which consists of about 20,000 rules, by using a classical theorem prover. We identified ten inaccuracies in the present knowledge base. One of the inaccuracies is presented and discussed here.

MedFrame provides a medical institution with a set of software tools for developing knowledge bases and inference mechanisms and applying them as expert systems in clinical routine. CADIAG-IV—a data-driven fuzzy expert system for computer-assisted consultation in internal medicine—is entirely based on MedFrame. MedFrame’s core components have been implemented; the implementation realization of CADIAG-IV and its application in clinical rheumatology is currently in progress. The achieved results confirm the applicability and scalability of the MedFrame/CADIAG-IV approach.

Defined over sets of truth values V which are closed subsets of [0, 1] containing both 0 and 1, Gödel logics GV are prominent examples of many-valued logics. We investigate a first-order fragment of GV extended with ∆ that is powerful enough to formalize important properties of fuzzy rule-based systems. The satisfiability problem in this fragment is shown to be NP-complete for all GV, also in presence of an additional, involutive, negation. In contrast to the one-variable case, in the considered fragment only two infinite-valued Gödel logics extended with ∆ differ w.r.t. satisfiability. Only one of them enjoys the finite model property. Keywords: First-order Gödel logics, satisfiability, monadic logic, one-variable fragment, involutive negation