Predicting Treatment Attrition in Buprenorphine-Naloxone Therapy: A Machine Learning Approach Using Multi-Site EHR Data

Presentation Time: 08:30 AM - 08:45 AM

Abstract Keywords: Precision Medicine, Machine Learning, Clinical Decision Support
Primary Track: Applications

This study investigates buprenorphine-naloxone (BUP-NAL) treatment attrition by employing machine learning models with multi-site EHR data to predict six-month attrition rates. Comparative analysis between model predictions and clinician predictions underscores the efficacy of machine learning in predicting treatment attrition. The findings advance BUP-NAL treatment strategies, combining data analytics and clinical expertise to detect and assist individuals prone to early treatment discontinuation.

Speaker(s):
Fateme Nateghi Haredasht, PhD
Stanford University