Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19.

ABSTRACT: Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the

SUBMITTER: Kogan Y 

PROVIDER: S-EPMC9648491 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature