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Accurate time prediction of patients' critical events is crucial in urgentscenarios where timely decision-making is important. Though many studies haveproposed automatic prediction methods using Electronic Health Records (EHR),their coarse-grained time resolutions limit their practical usage in urgentenvironments such as the emergency department (ED) and intensive care unit(ICU). Therefore, in this study, we propose an hourly prediction method basedon self-supervised predictive coding and multi-modal fusion for two criticaltasks: mortality and vasopressor need prediction. Through extensiveexperiments, we prove significant performance gains from both multi-modalfusion and self-supervised predictive regularization, most notably infar-future prediction, which becomes especially important in practice. Ouruni-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.

Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution