An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records.

ABSTRACT: The goal of this study is to develop and validate a lightweight, interpretable machine learning (ML) classifier to identify opioid overdoses in emergency medical services (EMS) records. We conducted a comparative assessment of three feature engineering approaches designed for use with unstructured narrative data. Opioid overdose annotations were provided by two harm reduction paramedics and two supporting annotators trained to reliably match expert annotations. Candidate feature engineering techniques included term frequency-inverse document frequency (TF-IDF), a highly performant approach to concept vectorization, and a custom approach based on the count of empirically-identified keywords. Each feature set was trained using four model architectures: generalized linear model (GLM), Naïve B

SUBMITTER: Graham SS 

PROVIDER: S-EPMC10826931 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature