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     <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Prospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>BCNatal, a tertiary maternal\u2010foetal medicine centre (Barcelona, Spain).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>A total of 482 pregnant women diagnosed with preeclampsia between August 2013 and April 2024.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Maternal demographic, clinical, ultrasound and laboratory data were prospectively collected, together with delivery details and maternal and neonatal complications. We generated a patient representation using maternal age, height, weight, body\u2010mass index, blood pressure, angiogenic factors, urinary albumin\/creatinine ratio, gestational age at birth and birthweight centile. Dimensionality reduction was performed using Uniform Manifold Approximation and Projection, followed by\n                      <jats:italic>k<\/jats:italic>\n                      \u2010means clustering to identify phenotypes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcome Measures<\/jats:title>\n                    <jats:p>Maternal and neonatal characteristics and complication rates were compared across clusters to evaluate the clinical significance of the data\u2010driven phenotypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Three phenotypes were identified. Cluster A (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009223; 46.2%) showed earlier delivery (mean 33.1\u2009\u00b1\u2009SD 3.3\u2009weeks), marked angiogenic imbalance, prevalent foetal growth restriction (64%) and the highest rates of maternal (23%) and neonatal (41%) complications. Cluster B (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009147; 30.5%) had later delivery (37.0\u2009\u00b1\u20092.1\u2009weeks), moderate angiogenic imbalance and intermediate birthweight centiles (15.5\u2009\u00b1\u20099.7). Cluster C (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009112; 23.2%) comprised mostly term cases (38.2\u2009\u00b1\u20091.5\u2009weeks) with the lowest angiogenic imbalance and the highest birthweight centile (67.9\u2009\u00b1\u200927.3); obesity (31%) and diabetes (15%) were most prevalent and maternal (4%) and neonatal (9%) complications were least frequent.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Unsupervised learning delineated three preeclampsia phenotypes. These phenotypes could support the need for future risk stratification and more personalised management; prospective external validation is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1471-0528.70262","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:25:51Z","timestamp":1778826351000},"page":"1926-1936","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study"],"prefix":"10.1111","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3393-813X","authenticated-orcid":false,"given":"Ohad","family":"Houri","sequence":"first","affiliation":[{"name":"BCNatal\u2014Barcelona Center for Maternal\u2010Fetal and Neonatal Medicine (Hospital Cl\u00ednic and Hospital Sant Joan de Deu), IDIBAPS University of Barcelona  Barcelona 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