Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness). Overall, discrimination based on variation in shape was more challenging than magnitude. Time-series forest-convolutional neural networks (TSF-CNN) and Gramian angular field(GAF)-CNN outperformed other approaches (P < 0.05) with overall area-under-the-curve (AUCs) of 0.93 for both models, and 0.92 and 0.89 for variation in magnitude and shape with up to 50% missing data. Furthermore, in a real-world assessment, the TSF-CNN model predicted T2D with AUCs reaching 0.72 using only BMI trajectories. In conclusion, we performed an extensive evaluation of DL approaches and identified robust modeling frameworks for disease prediction based on longitudinal clinical measurements.

来自电子健康记录的深度学习 (DL) 有望用于疾病预测，但尚未报道从模拟纵向临床测量中学习的系统方法。我们使用模拟的体重指数 (BMI)、葡萄糖和收缩压轨迹、独立隔离的形状和幅度变化比较了九个 DL 框架，并评估了各种参数（例如，不规则性、缺失）的模型性能。总体而言，基于形状变化的区分比大小更具挑战性。时间序列森林卷积神经网络 (TSF-CNN) 和 Gramian 角场 (GAF)-CNN 优于其他方法 ( P < 0.05)，两种模型的总体曲线下面积 (AUC) 为 0.93，幅度和形状变化为 0.92 和 0.89，缺失数据高达 50%。此外，在实际评估中，TSF-CNN 模型仅使用 BMI 轨迹预测 T2D，AUC 达到 0.72。总之，我们对深度学习方法进行了广泛的评估，并确定了基于纵向临床测量的疾病预测的稳健建模框架。