TY - GEN
T1 - On Mapping Textual Queries to a Common Data Model
AU - Liu, Sijia
AU - Wang, Yanshan
AU - Hong, Na
AU - Shen, Feichen
AU - Wu, Stephen
AU - Hersh, William
AU - Liu, Hongfang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/8
Y1 - 2017/9/8
N2 - The widespread adoption of Electronic Health Records (EHRs) has enabled data-driven approaches to clinical care and research. However, the performance and generalizability of those approaches are severely hampered by the lack of syntactic and semantic interoperability of EHR data across institutions. Towards resolving this problem, Common Data Models (CDMs) can be used to standardize the clinical data in clinical data repositories. In this paper, we described our mapping of entity mention types from patient-level information retrieval queries to an empirical subset of Observational Medical Outcomes Partnership (OMOP) CDM data fields. We investigated the empirical data model by annotating multi-institutional clinical data requests in free text and comparing the distributions of data model fields. The similar distribution of the entity mention types from two different sites indicates that the data model is generalizable for multi-institutional cohort identification queries.
AB - The widespread adoption of Electronic Health Records (EHRs) has enabled data-driven approaches to clinical care and research. However, the performance and generalizability of those approaches are severely hampered by the lack of syntactic and semantic interoperability of EHR data across institutions. Towards resolving this problem, Common Data Models (CDMs) can be used to standardize the clinical data in clinical data repositories. In this paper, we described our mapping of entity mention types from patient-level information retrieval queries to an empirical subset of Observational Medical Outcomes Partnership (OMOP) CDM data fields. We investigated the empirical data model by annotating multi-institutional clinical data requests in free text and comparing the distributions of data model fields. The similar distribution of the entity mention types from two different sites indicates that the data model is generalizable for multi-institutional cohort identification queries.
UR - http://www.scopus.com/inward/record.url?scp=85032365782&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032365782&partnerID=8YFLogxK
U2 - 10.1109/ICHI.2017.63
DO - 10.1109/ICHI.2017.63
M3 - Conference contribution
AN - SCOPUS:85032365782
T3 - Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
SP - 21
EP - 25
BT - Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
A2 - Cummins, Mollie
A2 - Facelli, Julio
A2 - Meixner, Gerrit
A2 - Giraud-Carrier, Christophe
A2 - Nakajima, Hiroshi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Healthcare Informatics, ICHI 2017
Y2 - 23 August 2017 through 26 August 2017
ER -