Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2.

ABSTRACT:

Objective

Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning.

Materials and methods

In this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four d

SUBMITTER: Wu H 

PROVIDER: S-EPMC7717299 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature