用于2型糖尿病（T2DM）可解释疾病轨迹的结构化因子隐马尔可夫模型（FHMM）

中文摘要 AI 辅助

本研究提出了因子隐马尔可夫模型（FHMM）的一种结构化变体，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、同步演化的组件的组合，这些组件与合并症和实验室检查结果相关联。这种结构化的潜在表示有助于识别具有临床意义的患者状态，并对常见疾病轨迹进行聚类。我们使用IQVIA医学研究数据（包含来自THIN的数据，THIN是Cegedim数据库中匿名化的电子健康记录（EHR）数据）对所提方法进行评估，识别出2006年1月至2019年12月期间首次开具非胰岛素类抗糖尿病药物（NIAD）处方的患者。该模型识别出多个与糖尿病相关并发症已知模式相对应的临床一致的潜在组件，并揭示了异质性进展通路，包括不同的微血管主导型和多器官疾病轨迹，这些轨迹与合并症负担升高及死亡率相关。这些结果表明，所提框架能够捕捉EHR数据中有意义的纵向结构，并为T2DM及其合并症的演变提供可解释的见解。

英文摘要

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.

发表机构

• Swiss Data Science Center (SDSC)（瑞士数据科学中心（SDSC））
• Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
• ETH Zürich（苏黎世联邦理工学院）
• Institute of Pharmaceutical Sciences（药学院）

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