Stanford, UCSF researchers predict newborn health outcomes using AI, EHR data

Researchers from Palo Alto, Calif.-based Stanford Medicine have developed a machine learning algorithm that scans EHRs to predict health outcomes for newborns.

The study, published Feb. 15 in Science Translational Medicine, linked EHRs from mothers at Stanford Health Care and their babies at Stanford Children's Health comprising 32,354 live births between 2014 and 2020. The algorithm, called a short-term memory neural network, provided strong predictions for which high-risk infants would develop certain complications.

"When we're reading, we don't remember every word, but we remember the key concepts, read the next part, add more key concepts and carry that forward," said senior study author Nima Aghaeepour, PhD, an associate professor of anesthesiology, perioperative and pain medicine and pediatrics at Stanford, in a Feb. 15 university news release. "The algorithm doesn't memorize the entire electronic health record of every patient, but it can remember key concepts and carry those forward to the point where we make a prediction."

The study also included researchers from University of California San Francisco.

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