ArticleNPJ digital medicine2025
Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Trustworthy AI for personalized glycemic control: a systematic review and critical appraisal of multimodal forecasting and safety-critical closed-loop control.Reviews in endocrine & metabolic disorders · 2026Review
- Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.Journal of materials science. Materials in medicine · 2026Review
- Glucose forecasting and hypoglycemia forewarning in type 1 and type 2 diabetes using deep learning.iScience · 2026Article
- Development and validation of an AI-driven blood glucose prediction system for pilots based on continuous glucose monitoring.Frontiers in physiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.