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By BeauHD from Slashdot's new-and-improved department:
February 13 '18 at 08:01 PM
An anonymous reader quotes a report from Scientific American: Clostridium difficile, a deadly bacterium spread by physical contact with objects or infected people, thrives in hospitals, causing 453,000 cases a year and 29,000 deaths in the United States, according to a 2015 study in the New England Journal of Medicine. Traditional methods such as monitoring hygiene and warning signs often fail to stop the disease. But what if it were possible to systematically target those most vulnerable to C-diff? Erica Shenoy, an infectious-disease specialist at Massachusetts General Hospital, and Jenna Wiens, a computer scientist and assistant professor of engineering at the University of Michigan, did just that when they created an algorithm to predict a patient's risk of developing a C-diff infection, or CDI. Using patients' vital signs and other health records, this method -- still in an experimental phase -- is something both researchers want to see integrated into hospital routines.
The CDI algorithm -- based on a form of artificial intelligence called machine learning -- is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning's predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University's Clinical Inference and Algorithms Program. Shenoy and Wiens' CDI algorithm analyzed a data set from 374,000 inpatient admissions to Massachusetts General Hospital and the University of Michigan Health System, seeking connections between cases of CDI and the circumstances behind them. The records contained over 4,000 distinct variables. As it repeatedly analyzes this data, the ML process extracts warning signs of disease that doctors may miss -- constellations of symptoms, circumstances and details of medical history most likely to result in infection at any point in the hospital stay.