锚点对比框架下短期血糖风险预测的可靠性评估研究
Reliability-Aware Short-Term Glycemic Risk Prediction Using an Anchor-Based Comparative Framework of Sequence, Multimodal, and State-Updating Digital-Twin Models
AI 导读
一项研究提出基于锚点的基准框架，评估序列、多模态与状态更新数字孪生模型对 1 型糖尿病患者 30、60、120 分钟低血糖和高血糖风险的短期预测。研究基于 T1DiabetesGranada 数据集，用 736 名患者构建 5,503,441 个标准化预测锚点，比较 Hybrid LSTM-Transformer、多模态数字孪生与状态更新数字孪生三种架构。
正文 · 原文
Abstract:Anticipating short-term hypoglycemic and hyperglycemic risk is essential for proactive diabetes management. However, existing machine learning approaches often use inconsistent evaluation protocols and emphasize predictive performance over reliability. This study introduces an anchor-based benchmarking framework integrating longitudinal glucose sequences and multimodal patient information. Using the T1DiabetesGranada dataset, 5,503,441 standardized prediction anchors were constructed from 736 individuals with type 1 diabetes to evaluate hypoglycemia and hyperglycemia prediction at 30-, 60-, and 120-minute horizons. Three neural architectures, a Hybrid LSTM-Transformer, a Multimodal Digital Twin, and a State-Updating Digital Twin with gap-aware longitudinal state progression, were compared under identical patient-level validation protocols. Beyond predictive performance, the evaluation examined calibration, classification thresholds, explanation reliability, cross-model disagreement, and robustness to controlled perturbations. No statistically significant predictive differences were observed after Holm correction, although the models exhibited complementary reliability characteristics. The Multimodal Digital Twin achieved the lowest mean Brier score, while the State-Updating Digital Twin demonstrated the lowest mean expected calibration error (ECE-10). The Hybrid LSTM-Transformer showed strong explanation faithfulness and stability under its temporal attribution protocol. These findings highlight the importance of evaluating digital-twin models beyond predictive accuracy and emphasize the need for reliability assessment and external validation before clinical deployment.
| Comments: | 36 pages, including figures, tables, and references. Research article on reliability-aware glycemic-risk prediction using sequence models and multimodal digital twins |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6; I.5.1; I.5.2; J.3 |
| Cite as: | arXiv:2610.36363 [cs.LG] |
| (or arXiv:2610.36363v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36363 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pir Bakhsh Khokhar [view email]
[v1]
Mon, 26 Oct 2026 22:28:12 UTC (3,103 KB)
来源：arXiv cs.AI · arxiv.org