Johns Hopkins spinoff launches clinical risk prediction platform 

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Bayesian Health, a health data startup created by Johns Hopkins researcher Suchi Saria, PhD, launched its artificial intelligence-powered clinical decision support platform on the commercial market July 12, according to a news release. 

Bayesian's AI platform works in conjunction with a hospital's EHR, analyzing available patient data and applying its AI and machine learning models to make clinical predictions in areas including clinical deterioration, sepsis, pressure injury and care transitions. 

The platform sends clinical signals within existing workflows when a critical moment is detected, which helps physicians and care team members accurately diagnose, intervene and deliver timely care. Bayesian is based on technology licensed from Baltimore-based Johns Hopkins University and has a foundation built on more than 21 patents and peer-reviewed research papers. 

Bayesian's sepsis module drove 1.85-hour faster antibiotic treatment for sepsis, according to a recent five-site study run by Johns Hopkins researchers and published in MedRxiv. Bayesian's sepsis prediction model also showed high buy-in from physicians and nurses, with an 89 percent adoption rate, according to the study. 

The company has raised $15 million in venture funding, led by Andreessen Horowitz, according to the news release.

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