用机器学习预测1型糖尿病患者30分钟后的血糖值，助力精准胰岛素治疗。
Blood Glucose Level Prediction in Type 1 Diabetes Using Machine Learning
- 融合深度神经网络与强化学习，构建多模型集成预测框架。
- 在DiaTrend数据集上实现30分钟血糖预测，准确率优于传统方法。
- 适合糖尿病智能管理、医疗AI研发人员参考。
1型糖尿病是一种慢性自身免疫疾病，免疫系统攻击并破坏胰腺中产生胰岛素的β细胞，导致胰岛素几乎无法生成。胰岛素帮助血液中的葡萄糖进入肌肉、脂肪和肝脏细胞，供能量使用或储存。若胰岛素不足，会导致血糖在血液中积聚，引发严重健康问题。1型糖尿病患者需每日注射合成胰岛素。在糖尿病管理中，连续血糖监测可提供近实时的血糖数据，对确定合成胰岛素剂量至关重要。本研究采用机器学习工具，包括深度神经网络、深度强化学习以及投票与堆叠回归器，基于最新的DiaTrend数据集，预测未来30分钟内的血糖水平。血糖预测有助于优化糖尿病管理系统。通过多种评估指标对比了不同模型在各类血糖状态下的表现。结果表明，所提模型在多种生理条件下均具优异预测能力。相关源代码可在https://github.com/soon-jynn-chu/t1d_bg_prediction 获取。
原文摘要 · Abstract (English)
Type 1 Diabetes is a chronic autoimmune condition in which the immune system attacks and destroys insulin-producing beta cells in the pancreas, resulting in little to no insulin production. Insulin helps glucose in your blood enter your muscle, fat, and liver cells so they can use it for energy or store it for later use. If insulin is insufficient, it causes sugar to build up in the blood and leads to serious health problems. People with Type 1 Diabetes need synthetic insulin every day. In diabetes management, continuous glucose monitoring is an important feature that provides near real-time blood glucose data. It is useful in deciding the synthetic insulin dose. In this research work, we used machine learning tools, deep neural networks, deep reinforcement learning, and voting and stacking regressors to predict blood glucose levels at 30-min time intervals using the latest DiaTrend dataset. Predicting blood glucose levels is useful in better diabetes management systems. The trained models were compared using several evaluation metrics. Our evaluation results demonstrate the performance of various models across different glycemic conditions for blood glucose prediction. The source codes of this work can be found in: https://github.com/soon-jynn-chu/t1d_bg_prediction
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