Peer-Reviewed Publication
J Forensic Sci2026;71(3):1329-1337.May 1, 2026Journal Article
Improving drug identification in overdose death surveillance by using clinical natural language processing models.
Arthur J Funnell1, Panayiotis Petousis1, Fabrice Harel-Canada2, Ruby Romero3, Alex A T Bui1, Adam Koncsol4, Hritika Chaturvedi3, Chelsea Shover4, David Goodman-Meza5
1Medical & Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
2Computer Science Department, University of California, Los Angeles, Los Angeles, California, USA.
3Computational and Systems Biology, University of California, Los Angeles, Los Angeles, California, USA.
4Division of General Internal Medicine and Health Services Research, University of California, Los Angeles, Los Angeles, California, USA.
5Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.
Abstract
The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical overdose data are often buried in free-text coroner reports, leading to delays and information loss when coded into ICD (International Classification of Disease)-10 classifications. Natural language processing (NLP) models may automate and enhance ov…
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