Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

ABSTRACT:

Background

Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.

Methods

We searched the peer-reviewed, indexed literature using Medline, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, CINAHL, Scopus, ACM Digital Library, Inspec, Web of Science's Science Citation Index, Social Sciences Citation Index, and the Emerging Sources Citation Index, up

SUBMITTER: Birdi S 

PROVIDER: S-EPMC11682638 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature