Abstract temporal diagnosis in medical domains

authored by

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.

Organisation(s)

Knowledge-Based Systems Section

External Organisation(s)

Eurac Research

Type

Article

Journal

Artificial Intelligence in Medicine

Volume

10

Pages

209-234

No. of pages

26

ISSN

0933-3657

Publication date

07.1997

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Medicine (miscellaneous), Artificial Intelligence

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1016/S0933-3657(97)00393-X (Access: Unknown)