Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.06835 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912385767309312 |
|---|---|
| author | Xu, Ziping Jajal, Hinal Choi, Sung Won Nahum-Shani, Inbal Shani, Guy Psihogios, Alexandra M. Hung, Pei-Yao Murphy, Susan |
| author_facet | Xu, Ziping Jajal, Hinal Choi, Sung Won Nahum-Shani, Inbal Shani, Guy Psihogios, Alexandra M. Hung, Pei-Yao Murphy, Susan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning on Dyads to Enhance Medication Adherence Xu, Ziping Jajal, Hinal Choi, Sung Won Nahum-Shani, Inbal Shani, Guy Psihogios, Alexandra M. Hung, Pei-Yao Murphy, Susan Machine Learning 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. |
| title | Reinforcement Learning on Dyads to Enhance Medication Adherence |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.06835 |