用多智能体强化学习优化医患二人组的用药提醒，提升青少年康复期服药依从性。
Reinforcement Learning on Dyads to Enhance Medication Adherence
- 设计双智能体强化学习框架，分别控制干预的两个核心环节
- 在真实临床数据模拟中使服药依从率提升约3%
- 适合关注数字健康、个性化干预的医疗与AI研究者
青少年和年轻成人（AYAs）在造血干细胞移植（HCT）后，药物依从性对康复至关重要。然而出院后，他们面临个体（如身体与情绪症状）和人际障碍（如与照护伙伴关系紧张），影响用药管理。为优化针对二人组及其关系的三成分数字干预方案，本文提出一种新型多智能体强化学习（MARL）方法，实现干预策略的个性化推送。每个智能体负责一个干预组件，结合领域知识，相较单智能体模型加速学习过程。基于真实临床数据构建的二人组仿真环境评估显示，该方法使药物依从率较完全随机干预提高约3%。该方法的有效性将在后续临床试验中进一步验证。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。