The use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review.

ABSTRACT:

Aims

Early identification of pharmacological therapy for gestational diabetes mellitus (GDM), a common pregnancy complication, through machine learning could allow for better therapeutic strategies and improved treatment efficiency. This scoping review aimed to comprehensively review the machine learning models used to predict the need for pharmacological therapy in GDM.

Methods

Four electronic databases-Embase, Medline, IEEE Xplore and Web of Science-were searched for publications between 1 July 2007 and 31 August 2024. Studies predicting pharmacological therapy for GDM using machine learning were included. The Joanna Briggs Institute and PRISMA-ScR checklist was followed, and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to assess quality.

Results

SUBMITTER: Kirkwood JR 

PROVIDER: S-EPMC12857867 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature