Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review.

ABSTRACT:

Introduction

There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning-based prediction model in preterm birth.

Material and methods

We conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD)

SUBMITTER: Yang Q 

PROVIDER: S-EPMC9780725 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature