Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs.

ABSTRACT: Unrecognized deterioration of COVID-19 patients 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 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 develop and compare two prognostic models that predict if a patient will experience deterioration in the forthcoming 3 to 24 h. The models sequentially process routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. These models are also provi

SUBMITTER: Mehrdad S 

PROVIDER: S-EPMC10282033 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature