论文标题
在野外学习胰岛素 - 葡萄糖动力学
Learning Insulin-Glucose Dynamics in the Wild
论文作者
论文摘要
我们开发了一种新的胰岛素 - 葡萄糖动力学模型，以预测1型糖尿病患者的血糖。我们通过引入由机器学习序列模型驱动的时变动态来增强现有的生物医学模型。我们的模型在生理上具有合理的归纳偏见和临床上可解释的参数（例如胰岛素灵敏度），同时继承了现代模式识别算法的灵活性。对成功建模至关重要的是通过序列模型的灵活但结构化表示。相反，LSTM（LSTM）较少的模型无法提供可靠或生理上合理的预测。我们进行了广泛的实证研究。我们表明，允许生物医学模型动力学随时间变化可长期改善预测，最长六个小时，并产生与胰岛素和碳水化合物的生理影响一致的预测。
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters -- e.g., insulin sensitivity -- while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.
