Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study.

ABSTRACT:

Background

Gestational diabetes (GDM) is prevalent and benefits from timely and effective treatment, given the short window to impact glycemic control. Clinicians face major barriers to choosing effectively among treatment modalities [medical nutrition therapy (MNT) with or without pharmacologic treatment (antidiabetic oral agents and/or insulin)]. We investigated whether clinical data at varied stages of pregnancy can predict GDM treatment modality.

Methods

Among a population-based cohort of 30,474 pregnancies with GDM delivered at Kaiser Permanente Northern California in 2007-2017, we selected those in 2007-2016 as the discovery set and 2017 as the temporal/future validation set. Potential predictors were extracted from electronic health records at different timepoints (le

SUBMITTER: Liao LD 

PROVIDER: S-EPMC9476287 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature