A Structural FHMM for Interpretable Disease Trajectories in T2DM

2026-08-25

AI Summary

研究提出一种结构化因子隐马尔可夫模型，用于分析2型糖尿病患者的疾病轨迹，通过电子健康记录识别临床有意义的患者状态和常见疾病路径。

Abstract

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.

Problem

• Type 2 diabetes mellitus
• disease trajectories
• latent health state
• electronic health records
• comorbidities

Innovation

• Structural FHMM
• Disease Trajectories
• T2DM
• Latent Components
• EHR Data