Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.

ABSTRACT: As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e., differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders ar

SUBMITTER: Chang T 

PROVIDER: S-EPMC10162497 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature