Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.

ABSTRACT:

Objective

To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties.

Design

Systematic review.

Data sources

PubMed from 1 January 2018 to 31 December 2019.

Eligibility criteria

Articles reporting on the development, with or without external validation, of a multivariable prediction model (diagnostic or prognostic) developed using supervised machine learning for individualised predictions. No restrictions applied for study design, data source, or predicted patient related health outcomes.

Review methods

Methodological quality of the studies was determined and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). This tool contains 21 signall

SUBMITTER: Andaur Navarro CL 

PROVIDER: S-EPMC8527348 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature