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Aligned-Layer Text Search in Clinical Notes
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 lik...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466869/ https://www.ncbi.nlm.nih.gov/pubmed/29295172 |
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author | Wu, Stephen Wen, Andrew Wang, Yanshan Liu, Sijia Liu, Hongfang |
author_facet | Wu, Stephen Wen, Andrew Wang, Yanshan Liu, Sijia Liu, Hongfang |
author_sort | Wu, Stephen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7466869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-74668692020-09-02 Aligned-Layer Text Search in Clinical Notes Wu, Stephen Wen, Andrew Wang, Yanshan Liu, Sijia Liu, Hongfang Stud Health Technol Inform Article 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. 2017 /pmc/articles/PMC7466869/ /pubmed/29295172 Text en http://creativecommons.org/licenses/by/4.0/ This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
spellingShingle | Article Wu, Stephen Wen, Andrew Wang, Yanshan Liu, Sijia Liu, Hongfang Aligned-Layer Text Search in Clinical Notes |
title | Aligned-Layer Text Search in Clinical Notes |
title_full | Aligned-Layer Text Search in Clinical Notes |
title_fullStr | Aligned-Layer Text Search in Clinical Notes |
title_full_unstemmed | Aligned-Layer Text Search in Clinical Notes |
title_short | Aligned-Layer Text Search in Clinical Notes |
title_sort | aligned-layer text search in clinical notes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466869/ https://www.ncbi.nlm.nih.gov/pubmed/29295172 |
work_keys_str_mv | AT wustephen alignedlayertextsearchinclinicalnotes AT wenandrew alignedlayertextsearchinclinicalnotes AT wangyanshan alignedlayertextsearchinclinicalnotes AT liusijia alignedlayertextsearchinclinicalnotes AT liuhongfang alignedlayertextsearchinclinicalnotes |