Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review.

ABSTRACT:

Background

While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

Methods

We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields. We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting

SUBMITTER: Andaur Navarro CL 

PROVIDER: S-EPMC8759172 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature