Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth.

ABSTRACT:

Background

Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.

Methods

Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.

Results

We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks

SUBMITTER: Abraham A 

PROVIDER: S-EPMC9516830 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature