We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining interpretability of the models. To go beyond the black box paradigm of artificial neural networks, we propose a parsimonious and robust semi-parametric approach (i.e., a landmarking competing risks model) that combines routinely collected low-resolution data with predictive features extracted from a convolutional neural network, that was trained on high resolution time-dependent information. We then use saliency maps to analyze and explain the extra predictive power of this model. To illustrate our methodology, we focus on healthcare-associated infections in patients admitted to an intensive care unit.

翻译：我们提出了一种新颖的方法论，用于将高分辨率纵向数据与生存模型的动态预测能力相结合。其目标具有双重性：在提升预测能力的同时保持模型的可解释性。为突破人工神经网络的黑箱范式，我们提出了一种简洁且稳健的半参数方法（即界标竞争风险模型），该模型将常规收集的低分辨率数据与从卷积神经网络中提取的预测特征相结合，后者基于高分辨率时间依赖性信息进行训练。随后，我们利用显著性图分析与解释该模型额外预测能力的来源。为阐明此方法论，我们重点关注重症监护病房住院患者的医疗相关感染问题。