论文标题
与多模式融合的自我监督预测性编码，用于精细粒度时间分辨率的患者恶化预测
Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution
论文作者
论文摘要
在及时决策很重要的紧急情况下，对患者关键事件的准确预测至关重要。尽管许多研究提出了使用电子健康记录（EHR）的自动预测方法，但它们粗粒的时间分辨率限制了其在紧急环境（例如急诊科（ED）和重症监护室（ICU））中的实际用途。因此，在这项研究中，我们提出了一种基于两项关键任务的自我监督预测编码和多模式融合的小时预测方法：死亡率和加压器需求需求。通过广泛的实验，我们证明了多模式融合和自我监督的预测正则化的显着性能，最值得注意的是在遥远的预测中，这在实践中变得尤为重要。我们的独立模式/双模式/双模式自我驾驶措施得分为0.846/0.877/0.897（0.824/0.855/0.886）和0.817/0.820/0.820/0.858（0.807/0.81/0.81/0.855），并需要（远距离验证）（远距离验证）（范围） AUROC分别。
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.
