Knowledge-based temporal abstraction in clinical domains.
We have defined a knowledge-based framework for the creation of abstract, interval-based concepts from time-stamped clinical data, the knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes its task into five subtasks; for each subtask we propose a formal solving mechanism. Our framework emphasizes explicit representation of knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse and sharing. The RESUME system implements the KBTA method. We tested RESUME in several clinical-monitoring domains, including the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert diabetes-therapy temporal-abstraction knowledge. Two diabetes-therapy experts (including the first one) created temporal abstractions from about 800 points of diabetic-patients' data. RESUME generated about 80% of the abstractions agreed by both experts; about 97% of the generated abstractions were valid. We discuss the advantages and limitations of the current architecture.
Shahar
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Section on Medical Informatics, School of Medicine, Stanford University, CA 94305-5479, USA. shahar@camis.stanford.edu
Musen
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MA
D001185:Artificial Intelligence; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006801:Humans; D013995:Time