[Knowledge-based diagnosis and therapeutic recommendations with fuzzy-set theory methods in patients with acute lung failure (ARDS)]. / Wissensbasierte Diagnostik und Therapieempfehlung mit Methoden der Fuzzy-Set-Theorie bei Patienten mit akutem Lungenversagen (ARDS).

OBJECTIVE: Since the treatment of patients with severe ARDS using the extracorporal lung assist (ECLA) methods remains a cost intensive and speculative procedure, a knowledge based computer system should be created and evaluated in order to support clinical decisions. METHODS: The model was based on the fuzzy set theory and therefore able to give decisions between yes and no, that means that a criterion could also be fulfilled to 35% or 80% for example. The development of this computer program consists of two steps: first, the entry criteria for the ECLA therapy were established within a framework of an international evaluation of clinical data from 3 centres (Berlin, Marburg, Vienna). Here, inherent vagueness, uncertainty of the occurrence and limited availability of medical data are to be considered to establish a useful tool. Secondly, this was done by grouping and weighting of parameters by the system and the status of each patient or patient group was assigned by the percentage of fulfillment of the criterion. RESULTS: By using a mixed sample of patients from these three centres, the fulfillment of entry criteria according either to definitions of Berlin or to definition of Marburg was different (68% versus 36%). Other differences (36% vs. 22% and 68% vs. 60%) were found between the fuzzy based score and the crisp score which represents the usually performed method. CONCLUSIONS: This now preevaluated minimal data set to describe severe ARDS patients based on the fuzzy set theory may be useful to evaluate patients for ECLA therapy or for another controlled ARDS-therapy.

The knowledge model of MedFrame/CADIAG-IV.

The medical consultation system MedFrame/CADIAG-IV is a successor of the prior CADIAG projects. It is the result of a complete redesign to account for today's demands on state-of-the-art software. Its knowledge representation and inference process are based on fuzzy set theory and fuzzy logic. Fuzzy sets are used for conversions from measured numeric values and observational data into symbolic ones. Medical relationships between findings, diseases, and therapies, the rules, are represented by fuzzy relations, that express positive or negative associations. Findings, diseases, and therapies are organised in hierarchies.

[New approaches to computer-assisted diagnosis of rheumatologic diseases]. / Neue Ansätze zur computerassistierten Diagnose rheumatologischer Erkrankungen.

UNLABELLED: Since the 1960s, several knowledge-based systems for computer-assisted diagnosis in radiology have been developed. The great majority of these tools has been implemented as off-line systems. This requires interaction with the system solely for the purpose of consultation and therefore interrupts the radiologist's work flow. This and inadequate man-machine interfaces may have inhibited the routine clinical use of such systems. The goal of this paper is to describe the current research toward the development of the on-line expert system Cadiag-4/Rheuma-Radio. The underlying fundamentals of the system design, including client/server architecture, communication interfaces, and fuzzy set theory and fuzzy logic as methods for knowledge representation and interference, are presented. METHODS: In radiology today, computers are routinely used to acquire radiological images in hospital and radiology information systems (HIS/RIS) and picture archiving and communication systems (PACS). In our approach, we make use of pre-existent sources of information to build an expert system that minimizes the interaction between radiologists and the computer. To handle uncertainty and vagueness of medical knowledge, fuzzy set theory and fuzzy logic are used. Given data of a specific case, a deductive inference procedure combines the observed radiological signs, establishes confirmed and excluded diagnoses as well as diagnostic hypotheses, and provides explanations for these conclusions. Furthermore, proposals for confirmation or exclusion of diagnostic hypotheses are offered. RESULTS: For evaluation purposes, an early prototype of Cadiag-4/Rheuma-Radio was tested on radiological disorders of the hip joint related to rheumatological diseases. Twenty radiological cases were used as test cases, reaching a diagnostic accuracy of about 80%. CONCLUSION: The first results are acceptable and encourage further work to cover the whole area of rheumatologically relevant radiological signs and diagnoses. Furthermore, research into the development of user-oriented data acquisition tools will be carried out.