{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T04:54:12Z","timestamp":1792212852966,"version":"4.4.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685335","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,22]]},"abstract":"<jats:p>Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning model using structured and unstructured electronic health record (EHR) data to predict when children in EDs might need intervention from child protective services. We used a case-control study design, analyzing data from a pediatric ED. Clinical notes were processed with natural language processing (NLP) techniques to identify suspected cases and matched in a 1:9 ratio to ensure dataset balance. The features from these notes were combined with structured EHR data to construct a model using the XGBoost algorithm. The model achieved a precision of 0.95, recall of 0.88, and F1-score of 0.92, with improvements seen from integrating NLP-derived data. Key indicators for abuse included hospital admissions, extended ED stays, and specific clinical orders. The model\u2019s accuracy and the utility of NLP suggest the potential for EDs to better identify at-risk children. Future work should validate the model further and explore additional features while considering ethical implications to aid healthcare providers in safeguarding children.<\/jats:p>","DOI":"10.3233\/shti240740","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T06:32:52Z","timestamp":1724394772000},"source":"Crossref","is-referenced-by-count":6,"title":["Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings"],"prefix":"10.3233","author":[{"given":"Aviv Y.","family":"Landau","sequence":"first","affiliation":[{"name":"School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashley","family":"Blanchard","sequence":"additional","affiliation":[{"name":"Columbia University Irving Medical Center, Department of Emergency Medicine, New York, NY, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paritosh","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"SAFElab, University of Pennsylvania, Philadelphia, PA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahad","family":"Althobaiti","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Columbia University, New York, NY, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Betina","family":"Idnay","sequence":"additional","affiliation":[{"name":"Columbia University Department of Biomedical Informatics, Columbia University, New York, NY, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Desmond U.","family":"Patton","sequence":"additional","affiliation":[{"name":"School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenrick","family":"Cato","sequence":"additional","affiliation":[{"name":"University of Pennsylvania School of Nursing, Children\u2019s Hospital of Philadelphia, Philadelphia, PA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2358-9837","authenticated-orcid":false,"given":"Maxim","family":"Topaz","sequence":"additional","affiliation":[{"name":"Columbia University School of Nursing, Columbia University Data Science Institute, Columbia University, New York, NY, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240740","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T06:32:53Z","timestamp":1724394773000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240740"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240740","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}