Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | MEDINFO 2017 |
| Subtitle of host publication | Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics |
| Publisher | IOS Press |
| Pages | 629-633 |
| Number of pages | 5 |
| Volume | 245 |
| ISBN (Electronic) | 9781614998297 |
| DOIs | |
| State | Published - Jan 1 2017 |
| Event | 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China Duration: Aug 21 2017 → Aug 25 2017 |
Publication series
| Name | Studies in Health Technology and Informatics |
|---|---|
| Volume | 245 |
| ISSN (Print) | 0926-9630 |
| ISSN (Electronic) | 1879-8365 |
Other
| Other | 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 |
|---|---|
| Country | China |
| City | Hangzhou |
| Period | 8/21/17 → 8/25/17 |
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Keywords
- Electronic health records
- Information storage and retrieval
- Natural language processing
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
- Health Information Management
Cite this
Aligned-layer text search in clinical notes. / Wu, Stephen; Wen, Andrew; Wang, Yanshan; Liu, Sijia; Liu, Hongfang D.
MEDINFO 2017: Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics. Vol. 245 IOS Press, 2017. p. 629-633 (Studies in Health Technology and Informatics; Vol. 245).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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 D
PY - 2017/1/1
Y1 - 2017/1/1
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
UR - http://www.scopus.com/inward/citedby.url?scp=85040524562&partnerID=8YFLogxK
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
VL - 245
T3 - Studies in Health Technology and Informatics
SP - 629
EP - 633
BT - MEDINFO 2017
PB - IOS Press
ER -