Identifying patients with significant problems related to social determinants of health with natural language processing

David Dorr, Cosmin A. Bejan, Christie Pizzimenti, Sumeet Singh, Matt Storer, Ana Quinones

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages1456-1457
Number of pages2
ISBN (Electronic)9781643680026
DOIs
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

Fingerprint

Natural Language Processing
Social Determinants of Health
Electronic Health Records
Health
Vital Signs
Processing

Keywords

  • Natural Language Processing
  • Social Determinants of Health

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Dorr, D., Bejan, C. A., Pizzimenti, C., Singh, S., Storer, M., & Quinones, A. (2019). Identifying patients with significant problems related to social determinants of health with natural language processing. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 1456-1457). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190482

Identifying patients with significant problems related to social determinants of health with natural language processing. / Dorr, David; Bejan, Cosmin A.; Pizzimenti, Christie; Singh, Sumeet; Storer, Matt; Quinones, Ana.

MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 1456-1457 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dorr, D, Bejan, CA, Pizzimenti, C, Singh, S, Storer, M & Quinones, A 2019, Identifying patients with significant problems related to social determinants of health with natural language processing. in B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 1456-1457, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 8/25/19. https://doi.org/10.3233/SHTI190482
Dorr D, Bejan CA, Pizzimenti C, Singh S, Storer M, Quinones A. Identifying patients with significant problems related to social determinants of health with natural language processing. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 1456-1457. (Studies in Health Technology and Informatics). https://doi.org/10.3233/SHTI190482
Dorr, David ; Bejan, Cosmin A. ; Pizzimenti, Christie ; Singh, Sumeet ; Storer, Matt ; Quinones, Ana. / Identifying patients with significant problems related to social determinants of health with natural language processing. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. pp. 1456-1457 (Studies in Health Technology and Informatics).
@inproceedings{100fc5febdbd4a89901b6b24be80622b,
title = "Identifying patients with significant problems related to social determinants of health with natural language processing",
abstract = "Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients.",
keywords = "Natural Language Processing, Social Determinants of Health",
author = "David Dorr and Bejan, {Cosmin A.} and Christie Pizzimenti and Sumeet Singh and Matt Storer and Ana Quinones",
year = "2019",
month = "8",
day = "21",
doi = "10.3233/SHTI190482",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "1456--1457",
editor = "Brigitte Seroussi and Lucila Ohno-Machado and Lucila Ohno-Machado and Brigitte Seroussi",
booktitle = "MEDINFO 2019",

}

TY - GEN

T1 - Identifying patients with significant problems related to social determinants of health with natural language processing

AU - Dorr, David

AU - Bejan, Cosmin A.

AU - Pizzimenti, Christie

AU - Singh, Sumeet

AU - Storer, Matt

AU - Quinones, Ana

PY - 2019/8/21

Y1 - 2019/8/21

N2 - Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients.

AB - Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients.

KW - Natural Language Processing

KW - Social Determinants of Health

UR - http://www.scopus.com/inward/record.url?scp=85071509713&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071509713&partnerID=8YFLogxK

U2 - 10.3233/SHTI190482

DO - 10.3233/SHTI190482

M3 - Conference contribution

C2 - 31438179

AN - SCOPUS:85071509713

T3 - Studies in Health Technology and Informatics

SP - 1456

EP - 1457

BT - MEDINFO 2019

A2 - Seroussi, Brigitte

A2 - Ohno-Machado, Lucila

A2 - Ohno-Machado, Lucila

A2 - Seroussi, Brigitte

PB - IOS Press

ER -