Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important. Though many studies have proposed automatic prediction methods using Electronic Health Records (EHR), their coarse-grained time resolutions limit their practical usage in urgent environments such as the emergency department (ED) and intensive care unit (ICU). Therefore, in this study, we propose an hourly prediction method based on self-supervised predictive coding and multi-modal fusion for two critical tasks: mortality and vasopressor need prediction. Through extensive experiments, we prove significant performance gains from both multi-modal fusion and self-supervised predictive regularization, most notably in far-future prediction, which becomes especially important in practice. Our uni-modal/bi-modal/bi-modal self-supervision scored 0.846/0.877/0.897 (0.824/0.855/0.886) and 0.817/0.820/0.858 (0.807/0.81/0.855) with mortality (far-future mortality) and with vasopressor need (far-future vasopressor need) prediction data in AUROC, respectively.

翻译：准确预测患者的危重事件时间在需要及时决策的紧急场景中至关重要。尽管已有诸多研究利用电子健康记录（EHR）提出自动预测方法，但这些方法的时间分辨率较粗，限制了其在急诊科（ED）和重症监护室（ICU）等紧急环境中的实际应用。为此，本研究提出一种基于自监督预测编码与多模态融合的逐时预测方法，针对死亡率与血管升压药需求两项关键任务进行预测。通过大量实验，我们证明了多模态融合与自监督预测正则化均能显著提升性能，尤其在远期预测中表现突出——这在临床实践中尤为重要。我们的单模态/双模态/双模态自监督方法在死亡率（远期死亡率）和血管升压药需求（远期血管升压药需求）预测数据上的AUROC分别达到0.846/0.877/0.897（0.824/0.855/0.886）与0.817/0.820/0.858（0.807/0.81/0.855）。