Article

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

2020-07-16

Abstract excerpt

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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a95f08f6-759a-5f87-99d6-0271ee8e8ee5
DOI: 10.1101/2020.07.15.20154864

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birthDOI 10.1101/2020.07.15.20154864Read

Using Machine Learning and Administrative Data to Predict Premature BirthsSemantic proximity Read

Development and validation of a spontaneous preterm birth risk prediction algorithm based on maternal bioinformatics: A single-center retrospective studySemantic proximity Read

Differential Predictability of Preterm Birth Types: Strong Signals for Indicated Cases versus Limited Success in Spontaneous Preterm BirthSemantic proximity Read

Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learningSemantic proximity Read

Multi-omic biomarker discovery for spontaneous preterm birth prevention applying machine learning methodsSemantic proximity Read

Machine Learning Estimation of Gestational Age at Delivery Using Linked Mother-Infant Electronic Health Records Across Two Health SystemsSemantic proximity Read

Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health recordsSemantic proximity Read

A predictive model for spontaneous preterm birth based on clinical indicators, social-behavioral factors, and psychological determinantsSemantic proximity Read

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A Cautionary TaleSemantic proximity Read

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine LearningSemantic proximity Read

Associations with spontaneous and indicated preterm birth in a densely phenotyped EHR cohortSemantic proximity Read

Prediction of spontaneous preterm birth in pregnant women using machine learningSemantic proximity Read