AI 中文总结
针对纵向药物数据的方法学挑战，提出两阶段自监督框架LOPEL，通过模拟与HIV队列验证，可学习药物轨迹表示并识别临床有意义的亚组，助力风险分层与临床管理。
AI 中文摘要
多药联用（通常定义为同时使用多种药物）在老年人群中日益普遍，且与不良健康结局相关。受HIV感染者（PWH）纵向衰老研究的推动，本研究探讨药物使用随时间的演变及其对共病模式的表征。纵向药物数据存在诸多方法学挑战，包括高维度、稀疏性、不规则观测时间，以及药物间的结构化药理关系。现有方法通常针对特定任务，缺乏学习药物轨迹通用表示的统一框架。本文提出LOPEL（纵向多药嵌入学习，LOngitudinal Polypharmacy Embedding Learning），这是一个用于学习纵向药物数据低维表示的两阶段自监督框架。第一阶段，利用高斯过程模型学习就诊级嵌入，该模型通过解剖学治疗学化学（ATC）层级纳入药理相似性；第二阶段，通过建模随时间的轨迹并基于Wasserstein的表示定义相似性，构建受试者级嵌入，该表示捕捉时间动态与不确定性。模拟研究表明，在高维稀疏性和不规则采样的现实条件下，LOPEL可准确恢复潜在结构。在HIV感染者衰老队列的应用中，LOPEL识别出具有临床意义的亚组，这些亚组在药物使用的时间、组成和进展方面存在差异，凸显了与风险分层和临床管理相关的异质性。
英文摘要
Polypharmacy, commonly defined as the concurrent use of multiple medications, is increasingly prevalent in aging populations and is associated with adverse health outcomes. Motivated by longitudinal studies of aging people with HIV (PWH), we study how medication use evolves over time and characterizes multimorbidity patterns. Longitudinal medication data present substantial methodological challenges, including high dimensionality, sparsity, irregular observation times, and structured pharmacologic relationships among medications. Existing approaches are typically task-specific and lack a unified framework for learning general-purpose representations of medication trajectories. We propose LOPEL (LOngitudinal Polypharmacy Embedding Learning), a two-stage self-supervised framework for learning low-dimensional representations of longitudinal medication data. In the first stage, visit-level embeddings are learned using a Gaussian process model that incorporates pharmacologic similarity through the Anatomical Therapeutic Chemical hierarchy. In the second stage, subject-level embeddings are constructed by modeling trajectories over time and defining similarity through a Wasserstein-based representation capturing temporal dynamics and uncertainty. Simulation studies demonstrate that LOPEL accurately recovers latent structure under realistic conditions with high-dimensional sparsity and irregular sampling. In an application to aging cohorts of PWH, LOPEL identifies clinically meaningful subgroups that differ in the timing, composition, and progression of medication use, highlighting heterogeneity relevant for risk stratification and clinical management.
