Reinforcement Learning on Dyads to Enhance Medication Adherence
This addresses medication adherence for adolescents and young adults with care partners, but it is incremental as it builds on existing digital interventions with a specific method.
The paper tackled the problem of low medication adherence in adolescents and young adults after hematopoietic cell transplantation by developing a Multi-Agent Reinforcement Learning approach to personalize digital interventions for dyads, resulting in a 3% improvement in adherence compared to random delivery.
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.