A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies.

ABSTRACT:

Importance

During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (PLTs), to optimize the distribution of scarce therapeutics.

Objective

To evaluate whether a machine learning PLT-based method of scarce resource allocation optimizes the treatment benefit of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint.

Design, setting, and participants

This retrospective cohort study used electronic health record data from October 1, 2021, to December 11, 2021, for the training cohort and data from June 1, 2021, to October 1, 2021, for the testing cohort. The coh

SUBMITTER: Xiao M 

PROVIDER: S-EPMC11400220 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature