CHAT WITH A LAPTOP –
Mar. 27, 2026 – “It would have been virtually impossible to analyze so many treatment records without AI/ML assistance,” Becker said.
Based on the findings, Becker recommends that state governments prioritize behavioral health services and work collaboratively to expand access to longer-duration, clinically appropriate treatment programs. Increasing availability—especially in states with limited treatment infrastructure—could significantly improve recovery outcomes nationwide.
Becker, who recently received a pilot project award from PIKO (Center for Pacific Innovations, Knowledge and Opportunities), plans to build on the research by examining local data on addiction treatment and recovery among Native Hawaiians and Pacific Islanders. “We developed and used an ensemble machine learning model called Random Forest Model with the aim to predict the 10 most important features that increase the likelihood of positive treatment outcomes.” The analysis found the most important factor associated with positive outcomes was how long an individual remains in treatment, regardless of setting. According to Becker, longer engagement significantly increases the likelihood of reducing or stopping substance use.
Other key factors included treatment accessibility, depending on clinical need, treatment type at entry and at discharge, housing status, participation in self-help groups, employment status and referral source.
