Ramazi Ramin, Perndorfer Christine, Soriano Emily C, Laurenceau Jean-Philippe, Beheshti Rahmatollah
Department of Computer & Informational Sciences, University of Delaware, Newark, DE, USA.
Department of Psychological & Brain Sciences, University of Delaware, Newark, DE, USA.
Smart Health (Amst). 2021 Jul;21. doi: 10.1016/j.smhl.2021.100206. Epub 2021 Jun 12.
Type 2 diabetes - a prevalent chronic disease worldwide - increases risk for serious health consequences including heart and kidney disease. Forecasting diabetes progression can inform disease management strategies, thereby potentially reducing the likelihood or severity of its consequences. We use continuous glucose monitoring and actigraphy data from 54 individuals with Type 2 diabetes to predict their future hemoglobin A1c, HDL cholesterol, LDL cholesterol, and triglyceride levels one year later. We use a combination of convolutional and recurrent neural networks to develop a deep neural network architecture that can learn the dynamic patterns in different sensors' data and combine those patterns with additional demographic and lab data. To further demonstrate the generalizability of our models, we also evaluate their performance using an independent public dataset of individuals with Type 1 diabetes. In addition to diabetes, our approach could be useful for other serious and chronic physical illness, where dynamic (e.g., from multiple sensors) and static (e.g., demographic) data are used for creating predictive models.
2型糖尿病是一种在全球范围内普遍存在的慢性疾病，会增加包括心脏病和肾病在内的严重健康后果的风险。预测糖尿病进展可以为疾病管理策略提供信息，从而有可能降低其后果的可能性或严重程度。我们使用来自54名2型糖尿病患者的连续血糖监测和活动记录仪数据来预测他们一年后的未来糖化血红蛋白、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇和甘油三酯水平。我们结合卷积神经网络和循环神经网络来开发一种深度神经网络架构，该架构可以学习不同传感器数据中的动态模式，并将这些模式与其他人口统计学和实验室数据相结合。为了进一步证明我们模型的通用性，我们还使用一个独立的1型糖尿病患者公共数据集评估了它们的性能。除了糖尿病，我们的方法对于其他严重的慢性身体疾病也可能有用，在这些疾病中，动态（例如来自多个传感器）和静态（例如人口统计学）数据被用于创建预测模型。
