Predicting clinical outcomes and hospitalization stay of hospitalized COVID-19 patients by using Deep Learning methods

Abstract

AbstractPredicting outcomes and other critical clinical events of hospitalized COVID-19 patients may provide a valuable asset to healthcare and a chance to improve patient outcomes. Here, we have analyzed over 10,000 hospitalized COVID-19 patients in the Houston Methodist Hospital at the Texas Medical Center from the beginning of pandemics till April of 2020. This work extends our previous study analyzing longitudinal symptomatics of the hospitalized patients by seeking to understand how standard patient clinical data, like demographics and comorbidities, together with symptom data from early hospitalization can be used to predict the clinical outcomes and hospitalization stay. Deep Learning (DL) classification and regression methods were applied to quantify patient record importance and to perform predictions. The results suggest that patient outcome can be predicted with up to 75% accuracy. However, the prediction of hospitalization stay was more complex indicating deeper optimization of features.