  Unrecognized patient deterioration can lead to high morbidity and mortality.
Most existing deterioration prediction models require a large number of
clinical information, typically collected in hospital settings, such as medical
images or comprehensive laboratory tests. This is infeasible for telehealth
solutions and highlights a gap in deterioration prediction models that are
based on minimal data, which can be recorded at a large scale in any clinic,
nursing home, or even at the patient's home. In this study, we propose and
develop a prognostic model that predicts if a patient will experience
deterioration in the forthcoming 3-24 hours. The model sequentially processes
routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c)
temperature. The model is also provided with basic patient information,
including sex, age, vaccination status, vaccination date, and status of
obesity, hypertension, or diabetes. We train and evaluate the model using data
collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA.
The model achieves an area under the receiver operating characteristic curve
(AUROC) of 0.808-0.880 for 3-24 hour deterioration prediction. We also conduct
occlusion experiments to evaluate the importance of each input feature, where
the results reveal the significance of continuously monitoring the variations
of the vital signs. Our results show the prospect of accurate deterioration
forecast using a minimum feature set that can be relatively easily obtained
using wearable devices and self-reported patient information.
