Extracting social determinants of health from electronic health records using natural language processing: a systematic review.
electronic health records
information extraction
machine learning
natural language processing
population health outcomes
social determinants of health
Journal
Journal of the American Medical Informatics Association : JAMIA
ISSN: 1527-974X
Titre abrégé: J Am Med Inform Assoc
Pays: England
ID NLM: 9430800
Informations de publication
Date de publication:
25 11 2021
25 11 2021
Historique:
received:
29
04
2021
revised:
09
07
2021
accepted:
04
08
2021
pubmed:
7
10
2021
medline:
22
1
2022
entrez:
6
10
2021
Statut:
ppublish
Résumé
Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.
Identifiants
pubmed: 34613399
pii: 6382241
doi: 10.1093/jamia/ocab170
pmc: PMC8633615
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2716-2727Subventions
Organisme : NIMH NIH HHS
ID : R01 MH121922
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH121924
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK092949
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH121907
Pays : United States
Organisme : NIMH NIH HHS
ID : R41 MH124581
Pays : United States
Organisme : NIMHD NIH HHS
ID : R25 MD011713
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH119177
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM105688
Pays : United States
Organisme : NIH HHS
ID : R01MH119177
Pays : United States
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.
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