用生理数据+机器学习提升儿童低血糖预测准确率
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes
- 融合可穿戴设备生理信号，不只依赖血糖数据
- 模型在自建数据集上达到0.75的AUROC，迁移学习后提升至0.78
- 适合儿科糖尿病管理与智能预警系统研发者参考
死于睡眠综合征描述了无长期并发症史的1型糖尿病（T1D）青少年突然猝死的现象。目前主流假说认为其与夜间低血糖（NH）密切相关。本研究旨在通过整合可穿戴传感器采集的生理指标与机器学习技术，提升儿童T1D患者夜间低血糖的预测能力。研究分析了来自16名儿童的内部数据集，探索特征工程、模型选择、网络架构及过采样策略对预测性能的影响。针对数据量有限的问题，采用公开成人数据集进行迁移学习。最终在自建数据集上实现0.75±0.21的AUROC，迁移学习后提升至0.78±0.05。该研究突破了仅依赖血糖值的预测模式，展示了机器学习在提高儿科糖尿病管理中夜间低血糖检测能力方面的潜力。
原文摘要 · Abstract (English)
The dead-in-bed syndrome describes the sudden and unexplained death of young individuals with Type 1 Diabetes (T1D) without prior long-term complications. One leading hypothesis attributes this phenomenon to nocturnal hypoglycemia (NH), a dangerous drop in blood glucose during sleep. This study aims to improve NH prediction in children with T1D by leveraging physiological data and machine learning (ML) techniques. We analyze an in-house dataset collected from 16 children with T1D, integrating physiological metrics from wearable sensors. We explore predictive performance through feature engineering, model selection, architectures, and oversampling. To address data limitations, we apply transfer learning from a publicly available adult dataset. Our results achieve an AUROC of 0.75 +- 0.21 on the in-house dataset, further improving to 0.78 +- 0.05 with transfer learning. This research moves beyond glucose-only predictions by incorporating physiological parameters, showcasing the potential of ML to enhance NH detection and improve clinical decision-making for pediatric diabetes management.
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