Details
| Original language | English |
|---|---|
| Pages (from-to) | 209-234 |
| Number of pages | 26 |
| Journal | Artificial Intelligence in Medicine |
| Volume | 10 |
| Issue number | 3 |
| Publication status | Published - Jul 1997 |
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.
Keywords
- Diagnosis, Model-based reasoning, Temporal reasoning
ASJC Scopus subject areas
- Medicine(all)
- Medicine (miscellaneous)
- Computer Science(all)
- Artificial Intelligence
Sustainable Development Goals
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In: Artificial Intelligence in Medicine, Vol. 10, No. 3, 07.1997, p. 209-234.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Abstract temporal diagnosis in medical domains
AU - Gamper, Johann
AU - Nejdl, Wolfgang
PY - 1997/7
Y1 - 1997/7
N2 - 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.
AB - 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.
KW - Diagnosis
KW - Model-based reasoning
KW - Temporal reasoning
UR - http://www.scopus.com/inward/record.url?scp=0031193522&partnerID=8YFLogxK
U2 - 10.1016/S0933-3657(97)00393-X
DO - 10.1016/S0933-3657(97)00393-X
M3 - Article
C2 - 9232186
AN - SCOPUS:0031193522
VL - 10
SP - 209
EP - 234
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
SN - 0933-3657
IS - 3
ER -