Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort.

ABSTRACT:

Background

Hypertensive disorders of pregnancy (HDP) are significant drivers of maternal and neonatal morbidity and mortality. Current management strategies include early identification and initiation of risk mitigating interventions facilitated by a rules-based checklist. Advanced analytic techniques, such as machine learning, can potentially offer improved and refined predictive capabilities.

Objective

To develop and internally validate a machine learning prediction model for hypertensive disorders of pregnancy (HDP) when initiating prenatal care.

Study design

We developed a prediction model using data from the prospective multisite cohort Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) among low-risk individuals without a prior history of a

SUBMITTER: Schor JS 

PROVIDER: S-EPMC11462053 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature