loading page

Machine learning to predict COVID-19 outcomes to facilitate decision making
  • Sonu Subudhi,
  • Ashish Verma,
  • Ankit Patel
Sonu Subudhi
Massachusetts General Hospital
Author Profile
Ashish Verma
Brigham and Women's Hospital
Author Profile
Ankit Patel
Brigham and Women's Hospital
Author Profile

Abstract

An increasing number of COVID-19 cases worldwide has overwhelmed the healthcare system. Physicians are struggling to allocate resources and to focus their attention on high-risk patients, partly because early identification of high-risk individuals is difficult. This can be attributed to the fact that COVID-19 is a novel disease and its pathogenesis is still partially understood. However, machine learning algorithms have the capability to correlate a large number of parameters within a short period of time to identify the predictors of disease outcome. Implementing such an algorithm to predict high-risk individuals during the early stages of infection, would be helpful in decision making for clinicians. Here, we propose recommendations to integrate machine learning model with electronic health records so that a real-time risk score can be developed for COVID-19.

Peer review status:ACCEPTED

06 Jun 2020Submitted to International Journal of Clinical Practice
06 Jun 2020Submission Checks Completed
06 Jun 2020Assigned to Editor
07 Jun 2020Reviewer(s) Assigned
20 Jun 2020Review(s) Completed, Editorial Evaluation Pending
24 Jun 20201st Revision Received
16 Jul 2020Submission Checks Completed
16 Jul 2020Assigned to Editor
16 Jul 2020Reviewer(s) Assigned
03 Aug 2020Review(s) Completed, Editorial Evaluation Pending
05 Aug 20202nd Revision Received
05 Aug 2020Submission Checks Completed
05 Aug 2020Assigned to Editor
05 Aug 2020Reviewer(s) Assigned
10 Aug 2020Review(s) Completed, Editorial Evaluation Pending
11 Aug 20203rd Revision Received
11 Aug 2020Submission Checks Completed
11 Aug 2020Assigned to Editor
13 Aug 2020Review(s) Completed, Editorial Evaluation Pending
16 Aug 2020Editorial Decision: Accept