Multi-Task Learning for Randomized Controlled Trials

Abstract

A randomized controlled trial (RCT) is used to study the safety and efficacy of new treatments, by comparing patient outcomes of an intervention group with a control group. Traditionally, RCTs rely on statistical analyses to assess the differences between the treatment and control groups. However, such statistical analyses are generally not designed to assess the impact of the intervention at an individual level. In this paper, we explore machine learning models in conjunction with an RCT for personalized predictions of a depression treatment intervention, where patients were longitudinally monitored with wearable devices. We formulate individual-level predictions in the intervention and control groups from an RCT as a multi-task learning (MTL) problem, and propose a novel MTL model specifically designed for RCTs. Instead of training separate models for the intervention and control groups, the proposed MTL model is trained on both groups, effectively enlarging the training dataset. We develop a hierarchical model architecture to aggregate data from different sources and different longitudinal stages of the trial, which allows the MTL model to exploit the commonalities and capture the differences between the two groups. We evaluated the MTL approach in an RCT involving 106 patients with depression, who were randomized to receive an integrated intervention treatment. Our proposed MTL model outperforms both single-task models and the traditional multi-task model in predictive performance, representing a promising step in utilizing data collected in RCTs to develop predictive models for precision medicine.

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