Abstract temporal diagnosis in medical domains.
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 viral 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-I is promising. The diagnosis computed in our system is often more precise than the diagnosis in HEPAXPERT-I and we detect inconsistent data sequences which cannot be detected in the latter system.
Gamper
J
J
University of Hannover, Institut für Rechnergestützie Wissensverarbeitung, Hannover, Germany.
Nejdl
W
W
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D006509:Hepatitis B / Q000175:diagnosis*; D006801:Humans; D008962:Models, Theoretical; D015203:Reproducibility of Results