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：在紧急情况下，精确预测患者的危急时间至关重要，因为及时的决策具有重要意义。虽然许多研究已经提出利用电子健康记录自动进行预测的方法，但它们的粗粒度时间分辨率限制了它们在紧急环境下（例如急诊科和重症监护室）的实用性。因此，在本研究中，我们提出了一种基于自监督预测编码和多模态融合的小时级预测方法，用于两个关键任务的病死率和血管加压素需求预测。通过广泛的实验证明了我们的多模态融合和自监督预测正则化对性能的显着提高，尤其是在远期预测方面，在实践中变得尤为重要。在AUROC方面，我们的单模态/双模态/双模态自监督分别在预测病死率（远期病死率）和血管加压素需求（远期血管加压素需求）时得分为0.846/0.877/0.897（0.824/0.855/0.886）和0.817/0.820/0.858（0.807/0.81/0.855）。