Sema4 and Mount Sinai use Machine Learning to Improve Postpartum Hemorrhage Risk Prediction

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[{"type":"text","content":"STAMFORD, Conn., Nov. 04, 2021 (GLOBE NEWSWIRE) -- Sema4 (NASDAQ: SMFR), an AI-driven genomic and clinical data intelligence platform company, recently published two studies demonstrating the utility of machine learning to predict clinical outcomes for postpartum hemorrhage (PPH). The studies, which will appear in a special “Informatics for Sex- and Gender-Related Health” print issue of the Journal of the American Medical Informatics Association (JAMIA), were conducted in collaboration with clinicians from the Mount Sinai Health System. Sema4 chose to focus its advanced machine learning methods on PPH as the condition is the leading cause of maternal mortality globally. PPH accounts for around a third of maternal deaths and often occurs in patients with no known risk factors for hemorrhage. In addition, limitations in diagnostic guidelines and risk assessment tools can make it difficult for healthcare providers to adequately identify and treat PPH, particularly in patients without evident symptoms. “These two new papers are among the first to use large-scale, comprehensive real-world data to predict clinical outcomes,” said Eric Schadt, PhD, Founder and Chief Executive Officer of Sema4 and joint corresponding author on the papers. “By implementing our predictive model into the clinical standard of care, healthcare providers may be able to improve PPH risk assessment and medical management for their pregnant patients resulting in better health outcomes.” The first study leveraged de-identified longitudinal electronic medical record (EMR) data on over 70,000 pregnancy deliveries at five Mount Sinai Health System hospitals to develop and validate a comprehensive digital phenotyping algorithm for PPH. The novel algorithm incorporates not only cumulative blood loss but also other critical diagnostic and treatment-related features indicative of PPH. “PPH is a devastating condition which occurs with little advance warning. Current guidelines primarily rely on cumulative blood loss as the main diagnostic marker for PPH,” said Li Li, MD, SVP of Clinical Informatics at Sema4 and joint corresponding author. “We identified additional clinical features from EMR data, enabling us to identify PPH with 89% accuracy, whereas the standard blood loss-based definition was only 67% accurate. Thus, we anticipate that our digital phenotyping algorithm...

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