Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing

KE Henry, R Adams, C Parent, H Soleimani… - Nature medicine, 2022 - nature.com
Abstract Machine learning-based clinical decision support tools for sepsis create
opportunities to identify at-risk patients and initiate treatments at early time points, which is …

Sociodemographic bias in clinical machine learning models: A scoping review of algorithmic bias instances and mechanisms

M Colacci, YQ Huang, G Postill, P Zhelnov… - Journal of Clinical …, 2024 - Elsevier
Background Clinical machine learning (ML) technologies can sometimes be biased and
their use could exacerbate health disparities. The extent to which bias is present, the groups …

The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare

SA Haider, S Borna, CA Gomez-Cabello… - Journal of Racial and …, 2024 - Springer
Methods Six electronic databases (PubMed, Scopus, IEEE, Google Scholar, EMBASE, and
Cochrane) were systematically searched on October 3, 2023. Inclusion criteria were peer …