Natural Language Processing - A Surveillance Stepping Stone to Identify Child Abuse.

May Shum¹, Allen Hsiao², Wei Teng³, Andrea Asnes², Joshua Amrhein⁴, Gunjan Tiyyagura²

¹Department of Pediatrics (M Shum, A Hsiao, A Asnes, and G Tiyyagura), Yale University School of Medicine, New Haven, Conn. Electronic address: shumm@chop.edu.

²Department of Pediatrics (M Shum, A Hsiao, A Asnes, and G Tiyyagura), Yale University School of Medicine, New Haven, Conn.

³Yale New Haven Hospital (W Teng), Joint Data Analytics Team, Conn.

⁴3M Health Information Systems (J Amrhein), Implementation/Adoption Services, Pittsburgh, Pa.

PubMed PMC DOI PDF

Abstract

OBJECTIVE: We aimed to refine a natural language processing (NLP) algorithm that identified injuries associated with child abuse and identify areas in which integration into a real-time clinical decision support (CDS) tool may improve clinical care. METHODS: We applied an NLP algorithm in "silent mode" to all emergency department (ED) provider notes between July 2021 and December 2022 (n = 353) a…

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Mentioned Companies

3M Health Information Systems
affiliation

NIH Funding

Grant #11167612
A Family Centered Approach to Intimate Partner Violence
YALE UNIVERSITY
$166,104 (FY2025)

Other Funding

NICHD NIH HHS(K23 HD107178)
United States
NCATS NIH HHS(UL1 TR001863)
United States

MeSH Terms

Humans0Child0Natural Language Processing0Child Abuse*Algorithms0Emergency Service, Hospital0Decision Support Systems, Clinical*Electronic Health Records0

Keywords

biaschild protection teamclinical decision supportguideline adherencenatural language processing

Article Info

PMID: 37652162
PMCID: PMC10840716
DOI: 10.1016/j.acap.2023.08.015
Received: May 23, 2023
Accepted: August 25, 2023

How to Cite

Shum M et al. Natural Language Processing - A Surveillance Stepping Stone to Identify Child Abuse. Acad Pediatr2024;24(1):92-96. doi:10.1016/j.acap.2023.08.015

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