Evaluation and enhancement of suspected opioid overdose definitions in emergency medical services data using machine learning with natural language processing - Peter Rock; Svetla Slavova; Sharon L. Walsh; Julia Martin; Daniel R. Harris (PLoS ONE, 2026)
Background Fatal and non-fatal drug overdoses have evolved into a critical public health crisis, with over a 50% increase in the rate of fatal drug overdose since 2019. Emergency Medical Services (EMS) data has advantages over traditional emergency department data, including timeliness and captured non-transport encounters. However, there is no consensus EMS definition for suspected opioid overdose (SOO), and currently implemented knowledge-based (KB) definition may miss ambiguous cases. Machine learning with natural language processing (ML-NLP) has the potential to enhance SOO identification. Methods Secondary data originated from an oversampled dataset of 2,327 weighted encounters from Kentucky State EMS data (2018–2022). EMS experts manually reviewed the records and determined ground truth SOO labels. We examined five commonly accepted KB definitions, ranging from narrow to highly inclusive criteria, spanning from structured-only data to combinations of structured and unstructured data. ML-NLP models were developed considering various EMS data fields and KB indicators. The models and KB definitions were evaluated using sensitivity, specificity, accuracy, precision, and F1-score. Results The ML-NLP models outperformed the KB definitions with the structured plus KB model achieving the highest F-score (0.81). Structured-only approaches demonstrated low sensitivity (0.30–0.45). The inclusion of patient care narratives and additional structured fields improved model performance with the ML-NLP models demonstrating high sensitivity (89.1%) and precision (89.0%). Conclusion Integrated ML-NLP approaches offer significant improvements in opioid overdose surveillance compared to structured-only, unstructured-only, and KB-only approaches. Future research should explore the generalizability of these models across different populations and geographic areas.