  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.
