Reinforcement Learning on Dyads to Enhance Medication Adherence
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
Medication adherence is critical for the recovery of adolescents and young
adults (AYAs) who have undergone hematopoietic cell transplantation (HCT).
However, maintaining adherence is challenging for AYAs after hospital
discharge, who experience both individual (e.g. physical and emotional
symptoms) and interpersonal barriers (e.g., relational difficulties with their
care partner, who is often involved in medication management). To optimize the
effectiveness of a three-component digital intervention targeting both members
of the dyad as well as their relationship, we propose a novel Multi-Agent
Reinforcement Learning (MARL) approach to personalize the delivery of
interventions. By incorporating the domain knowledge, the MARL framework, where
each agent is responsible for the delivery of one intervention component,
allows for faster learning compared with a flattened agent. Evaluation using a
dyadic simulator environment, based on real clinical data, shows a significant
improvement in medication adherence (approximately 3%) compared to purely
random intervention delivery. The effectiveness of this approach will be
further evaluated in an upcoming trial.
