Predicting opioid overdose risk using machine learning: Evaluation of an AI approach
Bo (Cloud) Cao & Giri Puligandla
This project’s aim was to address the opioid crisis by enhancing an existing AI/machine learning model that uses large-scale population health data to identify risk factors and predict opioid overdose (OpOD). Throughout the process, researchers collaborated closely with communities and stakeholders to gain insights on how these predictions could be applied meaningfully in real-world settings.
