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The formula for launching a machine learning company in health care looks something like this: Build a model, test it on historical patient data in a computer lab, and then start selling it to hospitals nationwide.

Suchi Saria, director of the machine learning and health care lab at Johns Hopkins University, is taking a different approach. Her company, Bayesian Health, is coming out of stealth mode on Monday by publishing a prospective study on how one of its lead products — an early warning system for sepsis — impacted the care of current patients in real hospitals.


The study found that use of the system was associated with a nearly two-hour reduction in the amount of time it took to deliver antibiotics to patients with sepsis that was confirmed after an alert. It is an encouraging sign about the ability of Bayesian’s product to reduce deaths from sepsis, a complication of infection that kills nearly 270,000 patients a year.

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