TY - JOUR
T1 - An architecture for linking medical decision-support applications to clinical databases and its evaluation
AU - German, Efrat
AU - Leibowitz, Akiva
AU - Shahar, Yuval
N1 - Funding Information:
This research was supported in part by NIH award no. LM-06806 and by Israeli Ministry of Defense Award BGU-No.89357628-01. We want to thank the staff of Ben-Gurion University’s medical-informatics laboratory for their useful comments. We also want to thank all of our collages at Stanford University and at the Palo Alto Veterans Administration Health Care Center, for their assistance in evaluating the IDAN architecture.
PY - 2009/4/1
Y1 - 2009/4/1
N2 - We describe and evaluate a framework, the Medical Database Adaptor (MEIDA), for linking knowledge-based medical decision-support systems (MDSSs) to multiple clinical databases, using standard medical schemata and vocabularies. Our solution involves a set of tools for embedding standard terms and units within knowledge bases (KBs) of MDSSs; a set of methods and tools for mapping the local database (DB) schema and the terms and units relevant to the KB of the MDSS into standardized schema, terms and units, using three heuristics (choice of a vocabulary, choice of a key term, and choice of a measurement unit); and a set of tools which, at runtime, automatically map standard term queries originating from the KB, to queries formulated using the local DB's schema, terms and units. The methodology was successfully evaluated by mapping three KBs to three DBs. Using a unit-domain matching heuristic reduced the number of term-mapping candidates by a mean of 71% even after other heuristics were used. Runtime access of 10,000 records required one second. We conclude that mapping MDSSs to different local clinical DBs, using the three-phase methodology and several term-mapping heuristics, is both feasible and efficient.
AB - We describe and evaluate a framework, the Medical Database Adaptor (MEIDA), for linking knowledge-based medical decision-support systems (MDSSs) to multiple clinical databases, using standard medical schemata and vocabularies. Our solution involves a set of tools for embedding standard terms and units within knowledge bases (KBs) of MDSSs; a set of methods and tools for mapping the local database (DB) schema and the terms and units relevant to the KB of the MDSS into standardized schema, terms and units, using three heuristics (choice of a vocabulary, choice of a key term, and choice of a measurement unit); and a set of tools which, at runtime, automatically map standard term queries originating from the KB, to queries formulated using the local DB's schema, terms and units. The methodology was successfully evaluated by mapping three KBs to three DBs. Using a unit-domain matching heuristic reduced the number of term-mapping candidates by a mean of 71% even after other heuristics were used. Runtime access of 10,000 records required one second. We conclude that mapping MDSSs to different local clinical DBs, using the three-phase methodology and several term-mapping heuristics, is both feasible and efficient.
KW - Databases
KW - Knowledge bases
KW - Mapping
KW - Medical decision-support systems
KW - Medical records systems
UR - http://www.scopus.com/inward/record.url?scp=61949486984&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2008.10.007
DO - 10.1016/j.jbi.2008.10.007
M3 - Article
AN - SCOPUS:61949486984
SN - 1532-0464
VL - 42
SP - 203
EP - 218
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 2
ER -