Customizing and fine-tuning opioid overdose machine learning models for community-specific dashboards.

Abstract

Drug overdose is a leading cause of death in the United States. Targeted community interventions and allocation of resources for reducing overdose deaths require timely and actionable data to make a meaningful public health impact. We present our work on tuning geographic-specific machine learning forecast models to predict the number of opioid-involved overdoses involving Emergency Medical Services (EMS) in Kentucky, with a focus on optimizing covariates representing measurable opioid overdose risk and protective factors. We aggregated suspected opioid-involved overdoses in EMS data into monthly totals by county between January 2017 and September 2025. We further grouped these counties into larger regions, representing Area Development Districts (ADDs), service areas for Community Mental Health Centers (CMHCs), and local health departments (LHDs). We constructed long-term time-series forecasting models (NLinear LTSF) to predict opioid-involved overdoses three months into the future. We tested the impact of using eight covariate data sources: Kentucky State Police drug seizures, Medicaid claims, community naloxone distribution, Kentucky Department of Corrections intakes and releases, social determinants of health data, location of treatment venues, and weather. We evaluated our models using both an unseen 10% test set and an expanding window strategy, where we simulated predicting historical data and retrained the model using data available for every three-month window. We embedded predictions into community-specific dashboards where end-users can select individual counties, or larger regions covered by distinct ADDs, CMHCs, or LHDs. The dashboards are integrated in Kentucky's Rapid Actionable Data for Opioid Response system designed to deliver ongoing near-real time analytics and forecasting at state and local levels.