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<p>Continuous prediction of glucose levels and hypoglycemia events is critical for managing type 1 diabetes mellitus (T1DM) under intensive insulin therapy. Existing models focus on a single task, limiting their practicality and adaptability in automated insulin delivery (AID) systems. To address this, a domain-agnostic continual multi-task learning (DA-CMTL) framework that simultaneously performs glucose level forecasting and hypoglycemia event classification within a unified framework is proposed. Trained on simulated datasets via Sim2Real transfer and adapted using elastic weight consolidation, DA-CMTL supports cross-domain generalization. Evaluation on public datasets (DiaTrend, OhioT1DM, and ShanghaiT1DM) yielded a root mean squared error of 14.01 mg/dL, mean absolute error of 10.03 mg/dL, and sensitivity/specificity of 92.13%/94.28% on 30 min prediction. Real-world validation using diabetes-induced rats demonstrated a reduction in time below range from 3.01% to 2.58%, supporting reliable integration as a safety layer in AID systems. These results highlight DA-CMTL’s robustness, scalability, and potential to improve safety in AID.</p>

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Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Minjoo Hwang,
  • Vega Pradana Rachim,
  • Junyoung Yoo,
  • Yein Lee,
  • Sung-Min Park

摘要

Continuous prediction of glucose levels and hypoglycemia events is critical for managing type 1 diabetes mellitus (T1DM) under intensive insulin therapy. Existing models focus on a single task, limiting their practicality and adaptability in automated insulin delivery (AID) systems. To address this, a domain-agnostic continual multi-task learning (DA-CMTL) framework that simultaneously performs glucose level forecasting and hypoglycemia event classification within a unified framework is proposed. Trained on simulated datasets via Sim2Real transfer and adapted using elastic weight consolidation, DA-CMTL supports cross-domain generalization. Evaluation on public datasets (DiaTrend, OhioT1DM, and ShanghaiT1DM) yielded a root mean squared error of 14.01 mg/dL, mean absolute error of 10.03 mg/dL, and sensitivity/specificity of 92.13%/94.28% on 30 min prediction. Real-world validation using diabetes-induced rats demonstrated a reduction in time below range from 3.01% to 2.58%, supporting reliable integration as a safety layer in AID systems. These results highlight DA-CMTL’s robustness, scalability, and potential to improve safety in AID.