Machine learning can predict preeclampsia by week 34 of pregnancy

A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a complication that can occur late in pregnancy.

The study was co-led by Dr. Fei Wang, associate dean for AI and data science and the Frances and John L. Loeb Professor of Medical Informatics in Department of Population Health Sciences at Weill Cornell Medicine, and Dr. Zhen Zhao, professor of clinical pathology and laboratory medicine at Weill Cornell Medicine and central laboratory director at NewYork-Presbyterian/Weill Cornell Medical Center. Clinical expertise in obstetrics was provided by Dr. Tracy Grossman, assistant professor of clinical obstetrics and gynecology at Weill Cornell Medicine and a maternal-fetal medicine specialist at NewYork-Presbyterian Brooklyn Methodist Hospital.

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