用结构化隐马尔可夫模型分析2型糖尿病病程，揭示不同进展路径。
A Structural FHMM for Interpretable Disease Trajectories in T2DM
- 将患者健康状态分解为多个独立演化的潜在成分，对应并发症与检验指标。
- 发现微血管主导和多器官受累两种异质性进展路径，与高共病负担相关。
- 结果可解释，适合临床研究者分析糖尿病长期演化规律。
本文提出一种结构化的因子隐马尔可夫模型（FHMM），用于分析2型糖尿病（T2DM）患者的疾病轨迹。该模型将患者的潜在健康状态表示为多个独立且同步演化的组件组合，分别关联共病情况和实验室检查结果。这种结构化潜变量表示有助于识别具有临床意义的患者状态，并聚类出常见的疾病演变路径。研究基于IQVIA医学研究数据，涵盖THIN数据库中匿名电子健康记录（EHR）数据，选取2006年1月至2019年12月间首次使用非胰岛素降糖药（NIAD）的患者。模型识别出多个与已知糖尿病并发症模式相符的临床合理潜成分，揭示了异质性进展路径，包括以微血管为主和涉及多器官的轨迹，后者与更高的共病负担和死亡率相关。结果表明，该框架能有效捕捉EHR数据中的纵向结构，并提供对T2DM及其共病演变过程的可解释洞察。
原文摘要 · Abstract (English)
In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Type 2 diabetes mellitus (T2DM). The model represents a patient's latent health state as a combination of multiple independent, simultaneously evolving components, associated with comorbidities and lab results. This structured latent representation facilitates the identification of clinically meaningful patient states and clustering of common disease trajectories. We evaluate the proposed approach using The IQVIA Medical Research Data incorporating data from THIN, a Cegedim database of anonymized electronic health records (EHR), identifying patients with a first-ever prescription for a non-insulin antidiabetic drug (NIAD) between January 2006 and December 2019. The model identifies multiple clinically coherent latent components corresponding to known patterns of diabetes-related complications and reveals heterogeneous progression pathways, including distinct microvascular-dominant and multi-organ trajectories associated with elevated comorbidity burden and mortality. These results demonstrate that the proposed framework captures meaningful longitudinal structure in EHR data and provides interpretable insights into the evolution of T2DM and its comorbidities.
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