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But a new algorithm developed by Johns Hopkins computer scientist Suchi Saria is being used at several Johns Hopkins hospitals to help diagnose the illness earlier and save lives. An AI expert and health AI pioneer, Suchi Saria's research has led to myriad new inventions to improve patient care. Her work first demonstrated the use of machine learning to make early detection possible in sepsis, a life-threatening condition (Science Trans. Med. 2015).

In selecting Saria for the 2017 list of the country's most promising young technology innovators, the magazine recognizes the Whiting School of Engineering assistant At the Johns Hopkins Hospital, in Baltimore, a similar system is showing much better results, says Suchi Saria, an assistant professor of computer science at Johns Hopkins University. Suchi Saria is an Associate Professor of Machine Learning and Healthcare at Johns Hopkins …New content will be added above the current area of focus upon selectionSuchi Saria is an Associate Professor of Machine Learning and Healthcare at Johns Hopkins University, where she uses big data to improve patient outcomes. Suchi Saria is the John C. Malone Associate Professor of computer science at the Whiting School of Engineering and of statistics and health policy at the Bloomberg School of Public Health. She directs the Machine Learning and Healthcare Lab and is the founding research director of the Malone Center for Engineering in Healthcare. PurposeSepsis Watch detects sepsis early, guides completion of appropriate treatment, and supports front-line providers with minimal interruption of cli Suchi Saria is the John C. Malone Assistant Professor of computer science at the Whiting School of Engineering and of statistics and health policy at the Bloomberg School of Public Health.

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2015-08-06 2015-08-05 Faster medical treatment saves lives. Machine Learning is already saving lives, by scouring a multitude of patients’ data and comparing them to one patient’s In 2015, Saria and her team first showed that a computer algorithm they developed could sift through patients’ records and predict septic shock—the deadliest version of sepsis—in 85% of cases, 2015-08-05 We believe that the single largest opportunity to improve patient care is through applying machine learning to multi-layered clinical data sets.

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Suchi Saria, right, with first author Katherine Henry (Will Kirk, Homewood Photo, Johns Hopkins University) 6 August 2015. Researchers at Johns Hopkins University wrote a computer model that gives clinicians an early and accurate warning that a patient is developing sepsis, a life-threatening complication of infections.

Department of Health Policy & Management. Contact: prefix@suffix where prefix=ssaria and suffix=cs.jhu.edu. Twitter: Follow @suchisaria.
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Contact: prefix@suffix where prefix=ssaria and suffix=cs.jhu.edu. Twitter: Follow @suchisaria. TY - JOUR.

Putting existing medical data to work to predict sepsis risk. Problem: Sometimes the difference between life and death is a quick and 2021-04-07 Suchi Saria Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality.
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N2 - Reinforcement learning is applied to two large databases of electronic health records for patients admitted to an intensive care unit to identify individualized treatment strategies for correcting hypotension in sepsis. 2015-08-06 2015-08-05 Faster medical treatment saves lives.

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https Saria, S. Individualized sepsis treatment using reinforcement learning.