Predicting Substance Use Treatment Failure with Transfer Learning

Abstract

Transfer learning, which involves repurposing a trained model on a related task, may allow for better predictions with substance use data than models that are trained using the target data alone. This approach may also be useful for small clinical datasets. The current study examined a method of classifying substance use treatment success using transfer learning. Transfer learning was used to classify data from a nationwide database. We trained a convolutional neural network on a heroin use treatment dataset, then trained and tested on a smaller opioid use treatment dataset. We compared this model with a baseline model that did not benefit from transfer learning, and a tuned random forest (RF). The goal was to see if model weights transfer across related substances and from large to small datasets. The transfer model outperformed the RF model and baseline model. These findings suggest leveraging the power of large datasets for transfer learning may be an effective approach in predicting substance use disorder (SUD) treatment outcomes. It is possible to achieve a score that performs better than RF using transfer learning.