TY - GEN
T1 - Aligned-layer text search in clinical notes
AU - Wu, Stephen
AU - Wen, Andrew
AU - Wang, Yanshan
AU - Liu, Sijia
AU - Liu, Hongfang
N1 - Funding Information:
This work is funded in part by the U.S. National Institutes of Health grant R01LM011934. Thanks to Dingcheng Li, who was involved in early work on layered language models.
PY - 2017
Y1 - 2017
N2 - Search techniques in clinical text need to make fine-grained semantic distinctions, since medical terms may be negated, about someone other than the patient, or at some time other than the present. While natural language processing (NLP) approaches address these fine-grained distinctions, a task like patient cohort identification from electronic health records (EHRs) simultaneously requires a much more coarse-grained combination of evidence from the text and structured data of each patient's health records. We thus introduce aligned-layer language models, a novel approach to information retrieval (IR) that incorporates the output of other NLP systems. We show that this framework is able to represent standard IR queries, formulate previously impossible multi-layered queries, and customize the desired degree of linguistic granularity.
AB - Search techniques in clinical text need to make fine-grained semantic distinctions, since medical terms may be negated, about someone other than the patient, or at some time other than the present. While natural language processing (NLP) approaches address these fine-grained distinctions, a task like patient cohort identification from electronic health records (EHRs) simultaneously requires a much more coarse-grained combination of evidence from the text and structured data of each patient's health records. We thus introduce aligned-layer language models, a novel approach to information retrieval (IR) that incorporates the output of other NLP systems. We show that this framework is able to represent standard IR queries, formulate previously impossible multi-layered queries, and customize the desired degree of linguistic granularity.
KW - Electronic health records
KW - Information storage and retrieval
KW - Natural language processing
UR - http://www.scopus.com/inward/record.url?scp=85040524562&partnerID=8YFLogxK
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U2 - 10.3233/978-1-61499-830-3-629
DO - 10.3233/978-1-61499-830-3-629
M3 - Conference contribution
C2 - 29295172
AN - SCOPUS:85040524562
T3 - Studies in Health Technology and Informatics
SP - 629
EP - 633
BT - MEDINFO 2017
A2 - Dongsheng, Zhao
A2 - Gundlapalli, Adi V.
A2 - Marie-Christine, Jaulent
PB - IOS Press
T2 - 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
Y2 - 21 August 2017 through 25 August 2017
ER -