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Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study.

BJOG : an international journal of obstetrics and gynaecology - 1 Sept 2026

Houri Ohad, Youssef Lina, Crovetto Francesca, Borrell Maria, Crimella Maddalena, Ferrante Maria Giulia, Novoa Rommy H, Casas Irene, Encabo Noelia, Benitez Leticia, Larroya Marta, Peguero Anna, Meler Eva, Castro-Barquero Sara, Bijnens Bart, Figueras Francesc, Gratacos Eduard, Bernardino Gabriel, Crispi Fàtima

Abstract excerpt

OBJECTIVE: To explore clinically meaningful phenotypes of preeclampsia using unsupervised machine learning. DESIGN: Prospective cohort study. SETTING: BCNatal, a tertiary maternal-foetal medicine centre (Barcelona, Spain). POPULATION: A total of 482 pregnant women diagnosed with preeclampsia between August 2013 and April 2024. METHODS: Maternal demographic, clinical, ultrasound and laboratory data were...

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Birth Weight
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Infant, Newborn

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Unsupervised Machine Learning

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PMID: 42136148
DOI: 10.1111/1471-0528.70262
PMCID: PMC13485454

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