Risk of bias in machine learning and statistical models to predict height or weight: a systematic review in fetal and paediatric medicine.

ABSTRACT:

Background

Prediction of suboptimal growth allows early intervention that can improve outcomes for developing fetus' as well as infants and children. We investigate the risk of bias in statistical or machine learning models to predict the height or weight of a fetus, infant or child under 20 years of age to inform the current standard of research and provide insight into why equations developed over 30 years ago are still recommended for use by national professional bodies.

Methods

We systematically searched MEDLINE and EMBASE for peer reviewed original research studies published in 2022. We included studies if they developed or validated a multivariable model to predict height or weight of an individual using two or more variables, excluding studies assessing imaging or usi

SUBMITTER: Lawrence NR 

PROVIDER: S-EPMC12703889 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature