Abstract temporal diagnosis in medical domains

verfasst von

Johann Gamper, Wolfgang Nejdl

Abstract

Most current model-based diagnosis formalisms and algorithms are defined only for static systems, which is often inadequate for medical reasoning. In this paper we describe a model-based framework plus algorithms for diagnosing time-dependent systems where we can define qualitative temporal scenarios. Complex temporal behavior is described within a logical framework extended by qualitative temporal constraints. Abstract observations aggregate from observations at time points to assumptions over time intervals. These concepts provide a very natural representation and make diagnosis independent of the number of actual observations and the temporal resolution. The concept of abstract temporal diagnosis captures in a natural way the kind of indefinite temporal knowledge which is frequently available in medical diagnoses. We use vital hepatitis B (including a set of real hepatitis B data) to illustrate and evaluate our framework. The comparison of our results with the results of HEPAXPERT-1 is promising. The diagnosis computed in our system is often more precise than the diagnosis in HEPAXPERT-1 and we detect inconsistent data sequences which cannot be detected in the latter system.

Organisationseinheit(en)

Fachgebiet Wissensbasierte Systeme

Externe Organisation(en)

Eurac Research

Typ

Artikel

Journal

Artificial Intelligence in Medicine

Band

10

Seiten

209-234

Anzahl der Seiten

26

ISSN

0933-3657

Publikationsdatum

07.1997

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Medizin (sonstige), Artificial intelligence

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1016/S0933-3657(97)00393-X (Zugang: Unbekannt)