Empirical assessment of bias in machine learning diagnostic test accuracy studies.

ABSTRACT:

Objective

Machine learning (ML) diagnostic tools have significant potential to improve health care. However, methodological pitfalls may affect diagnostic test accuracy studies used to appraise such tools. We aimed to evaluate the prevalence and reporting of design characteristics within the literature. Further, we sought to empirically assess whether design features may be associated with different estimates of diagnostic accuracy.

Materials and methods

We systematically retrieved 2 × 2 tables (n = 281) describing the performance of ML diagnostic tools, derived from 114 publications in 38 meta-analyses, from PubMed. Data extracted included test performance, sample sizes, and design features. A mixed-effects metaregression was run to quantify the association between design f

SUBMITTER: Crowley RJ 

PROVIDER: S-EPMC7647361 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature