We propose a robust in-time predictor for in-hospital COVID-19 patient's probability of requiring mechanical ventilation. A challenge in the risk prediction for COVID-19 patients lies in the great variability and irregular sampling of patient's vitals and labs observed in the clinical setting. Existing methods have strong limitations in handling time-dependent features' complex dynamics, either oversimplifying temporal data with summary statistics that lose information or over-engineering features that lead to less robust outcomes. We propose a novel in-time risk trajectory predictive model to handle the irregular sampling rate in the data, which follows the dynamics of risk of performing mechanical ventilation for individual patients. The model incorporates the Multi-task Gaussian Process using observed values to learn the posterior joint multi-variant conditional probability and infer the missing values on a unified time grid. The temporal imputed data is fed into a multi-objective self-attention network for the prediction task. A novel positional encoding layer is proposed and added to the network for producing in-time predictions. The positional layer outputs a risk score at each user-defined time point during the entire hospital stay of an inpatient. We frame the prediction task into a multi-objective learning framework, and the risk scores at all time points are optimized altogether, which adds robustness and consistency to the risk score trajectory prediction. Our experimental evaluation on a large database with nationwide in-hospital patients with COVID-19 also demonstrates that it improved the state-of-the-art performance in terms of AUC (Area Under the receiver operating characteristic Curve) and AUPRC (Area Under the Precision-Recall Curve) performance metrics, especially at early times after hospital admission.
翻译:我们建议对医院内COVID-19病人需要机械通风的概率进行可靠的实时预测。对COVID-19病人的风险预测中的一项挑战在于临床环境中观察到的病人生命和实验室的可变性和不规则抽样。现有方法在处理依赖时间特征的复杂动态方面有很大的局限性,要么是过于简化时间数据,带有失去信息的信息的简要统计数据,要么是导致不那么可靠结果的过度工程性能。我们提议了一个新的实时风险预测模型,处理数据中不规则的取样率,这要遵循个人病人进行机械通风的风险动态。多任务高斯进程包括多任务高斯进程,使用观测到的数值来学习远地点联合多变量有条件概率和实验室实验室在统一的时间网中推导出缺失的数值。时间估算数据被输入到一个多目标的自我保存网络中,提出一个新的定位编码预测层,用于实时预测。定位层输出在每一个用户对单个病人进行机械化通风的风险计分数,在医院内,在长期预测中显示一个稳定的轨道值,在长期的轨道上,在长期的轨道上,我们在整个实验室周期内进行一个最稳定的预测中,在不断学习。