Abstract
Physicians and medical decision-support applications, such as for diagnosis, therapy, monitoring, quality assessment, and clinical research, reason about patients in terms of abstract, clinically meaningful concepts, typically over significant time periods. Clinical databases, however, store only raw, time-stamped data. Thus, there is a need to bridge this gap. We introduce the Temporal Abstraction Language (TAR) which enables specification of abstract relations involving raw data and abstract concepts, and supports query answering. We characterize TAR knowledge bases that guarantee finite answer
sets and shortly explain why a complete bottom-up inference mechanism terminates. The TAR language was implemented as the inference component termed ALMA in the distributed mediation system IDAN, which integrates a set of clinical databases and medical knowledge bases. Initial experiments with
ALMA and IDAN on a large oncology-patients dataset are highly encouraging.
sets and shortly explain why a complete bottom-up inference mechanism terminates. The TAR language was implemented as the inference component termed ALMA in the distributed mediation system IDAN, which integrates a set of clinical databases and medical knowledge bases. Initial experiments with
ALMA and IDAN on a large oncology-patients dataset are highly encouraging.
| Original language | English GB |
|---|---|
| Title of host publication | Proceeding of the workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP) |
| State | Published - 2003 |