Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.

ABSTRACT:

Background

The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19.

Methods

Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collabo

SUBMITTER: Moradi H 

PROVIDER: S-EPMC9997963 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature