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<p>Deep learning models leveraging electronic health records (EHR) for opportunistic screening of type 2 diabetes (T2D) can improve current practices by identifying individuals who may need further glycemic testing. Accurate onset prediction and subtyping are crucial for targeted interventions, but existing methods treat the tasks separately, thus limiting clinical utility. In this paper, we introduce a novel deep metric learning (DML) model that unifies both tasks by learning a latent space based on sample similarity. In onset prediction, the DML model predicts the onset of T2D 7 years later with an AUC of 0.754, outperforming logistic regression (AUC 0.706), clinical risk factors (AUC 0.693), and glycemic measures (AUC 0.632). For subtyping, we identify three subtypes with varying prevalences of obesity-related, cardiovascular, and mental health conditions. Additionally, the subtype with fewer comorbidities shows earlier metformin initiation and a greater reduction in HbA1c. We validated these findings using data from 300 U.S. hospitals in the All of Us program (T2D, <i>n</i> = 7567) and the Massachusetts General Brigham Biobank (T2D, <i>n</i> = 3298), demonstrating the transferability of our model and subtypes across cohorts.</p>

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Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月25日

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  • Qixuan Jin,
  • Haoran Zhang,
  • Lukasz Szczerbinski,
  • Jiacheng Zhu,
  • Walter Gerych,
  • Xuhai Xu,
  • Kai Wang,
  • Sarah Hsu,
  • Ravi Mandla,
  • Aaron J. Deutsch,
  • Alisa Manning,
  • Josep M. Mercader,
  • Thomas Hartvigsen,
  • Miriam S. Udler,
  • Marzyeh Ghassemi

摘要

Deep learning models leveraging electronic health records (EHR) for opportunistic screening of type 2 diabetes (T2D) can improve current practices by identifying individuals who may need further glycemic testing. Accurate onset prediction and subtyping are crucial for targeted interventions, but existing methods treat the tasks separately, thus limiting clinical utility. In this paper, we introduce a novel deep metric learning (DML) model that unifies both tasks by learning a latent space based on sample similarity. In onset prediction, the DML model predicts the onset of T2D 7 years later with an AUC of 0.754, outperforming logistic regression (AUC 0.706), clinical risk factors (AUC 0.693), and glycemic measures (AUC 0.632). For subtyping, we identify three subtypes with varying prevalences of obesity-related, cardiovascular, and mental health conditions. Additionally, the subtype with fewer comorbidities shows earlier metformin initiation and a greater reduction in HbA1c. We validated these findings using data from 300 U.S. hospitals in the All of Us program (T2D, n = 7567) and the Massachusetts General Brigham Biobank (T2D, n = 3298), demonstrating the transferability of our model and subtypes across cohorts.