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

Daniel R Harris¹, Aaron Mullen¹, Peter Rock¹, Carmen Canedo^1,2, Katherine Thompson³, Michelle R Lofwall⁴, Cody Bumgardner¹, Jeffery Talbert⁵, Svetla Slavova²

¹Institute for Biomedical Informatics, College of Medicine, University of Kentucky, Lexington KY 40536, United States of America.

²Department of Biostatistics, College of Public Health, University of Kentucky, Lexington KY 40536, United States of America.

³Dr. Bing Zhang Department of Statistics, College of Arts and Sciences, University of Kentucky, Lexington KY 40536, United States of America.

⁴Departments of Behavioral Science and Psychiatry, College of Medicine, University of Kentucky, Lexington KY 40536, United States of America.

⁵Department of AI and Health, Medical College of Georgia at Augusta University, Augusta GA 30912, United States of America.

PubMed PMC DOI PDF

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 Servic…

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