  Leading up to August 2020, COVID-19 has spread to almost every country in the
world, causing millions of infected and hundreds of thousands of deaths. In
this paper, we first verify the assumption that clinical variables could have
time-varying effects on COVID-19 outcomes. Then, we develop a temporal
stratification approach to make daily predictions on patients' outcome at the
end of hospital stay. Training data is segmented by the remaining length of
stay, which is a proxy for the patient's overall condition. Based on this, a
sequence of predictive models are built, one for each time segment. Thanks to
the publicly shared data, we were able to build and evaluate prototype models.
Preliminary experiments show 0.98 AUROC, 0.91 F1 score and 0.97 AUPR on
continuous deterioration prediction, encouraging further development of the
model as well as validations on different datasets. We also verify the key
assumption which motivates our method. Clinical variables could have
time-varying effects on COVID-19 outcomes. That is to say, the feature
importance of a variable in the predictive model varies at different disease
stages.
