Medicine Overdose Prediction Using Machine Learning - Sedhumadhavan, S.; Gomathi, K.; Imayajothy, M.; Kamaleishuvari, A.P. (Journal of Neonatal Surgery, 2025)
The opioid addiction crisis in the United States has gained national attention due to the alarming rise in opioid overdose (OD)-related deaths. Addressing this epidemic has become a top priority for governments and healthcare providers, requiring critical insights into the risk factors associated with opioid overdose. In this paper, we present our work on developing machine learning-based prediction models to assess the likelihood of opioid overdose using patients’ electronic health records (EHR). We conducted two studies utilizing New York State claims data (SPARCS) with 440,000 patients and Cerner’s Health Facts database with 110,000 patients. Our experiments demonstrated that EHR-based prediction models achieved the highest recall using the random forest method (precision: 95.3%, recall: 85.7%, F1 score: 90.3%), while deep learning yielded the highest pre- cision (precision: 99.2%, recall: 77.8%, F1 score: 87.2%). Addi- tionally, we identified clinical events as key features contributing to the accuracy of these predictions.