ArticleAcademic pediatrics2022
Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.
Article in Academic pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Screening tools used by paediatric healthcare providers to identify child maltreatment by parents or caregivers: a systematic review.BMJ open · 2025Pooled it
- Identifying Risk Factors of Children Who Suffered Physical Abuse: A Systematic Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025Pooled it
- Conversational AI for Child Abuse Detection Through Multistage Counseling: Model Development and Validation Study.Journal of medical Internet research · 2026Article
- The use of ICD-10 coding for children hospitalized with suspected child physical abuse at a tertiary care pediatric hospital in Greece.European journal of pediatrics · 2025Article
- Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks.JMIR pediatrics and parenting · 2025Article
- Developing and Testing the Usability of a Novel Child Abuse Clinical Decision Support System: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Artificial Intelligence and Child Abuse and Neglect: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Estimating the Prevalence of Child Abuse and Neglect Among Adolescents in Primary Care Through Diagnoses Codes and Free-Text EHR Clinical Notes.Journal of pediatric health care : official publication of National Association of Pediatric Nurse Associates & PractitionersArticle
- Natural Language Processing - A Surveillance Stepping Stone to Identify Child Abuse.Academic pediatricsArticle
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 2 countries.
Funding
Abstract
objectivesMedically minor but clinically important findings associated with physical child abuse, such as bruises in pre-mobile infants, may be identified by frontline clinicians yet the association of these injuries with child abuse is often not recognized, potentially allowing the abuse to continue and even to escalate. An accurate natural language processing (NLP) algorithm to identify high-risk injuries in electronic health record notes could improve detection and awareness of abuse. The objectives were to: 1) develop an NLP algorithm that accurately identifies injuries in infants associated with abuse and 2) determine the accuracy of this algorithm.
methodsAn NLP algorithm was designed to identify ten specific injuries known to be associated with physical abuse in infants. Iterative cycles of review identified inaccurate triggers, and coding of the algorithm was adjusted. The optimized NLP algorithm was applied to emergency department (ED) providers' notes on 1344 consecutive sample of infants seen in 9 EDs over 3.5 months. Results were compared with review of the same notes conducted by a trained reviewer blind to the NLP results with discrepancies adjudicated by a child abuse expert.
resultsAmong the 1344 encounters, 41 (3.1%) had one of the high-risk injuries. The NLP algorithm had a sensitivity and specificity of 92.7% (95% confidence interval [CI]: 79.0%-98.1%) and 98.1% (95% CI: 97.1%-98.7%), respectively, and positive and negative predictive values were 60.3% and 99.8%, respectively, for identifying high-risk injuries.
conclusionsAn NLP algorithm to identify infants with high-risk injuries in EDs has good accuracy and may be useful to aid clinicians in the identification of infants with injuries associated with child abuse.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.