Medical Xpress November 17, 2021
By coupling machine learning with whole genome sequencing, University of Pittsburgh School of Medicine and Carnegie Mellon University scientists greatly improved the quick detection of infectious disease outbreaks within a hospital setting over traditional methods for tracking outbreaks.
The results, published today in the journal Clinical Infectious Diseases, indicate a way for health systems to identify and then stop hospital-based infectious disease outbreaks in their tracks, cutting costs and saving lives.
“The current method used by hospitals to find and stop infectious disease transmission among patients is antiquated. These practices haven’t changed significantly in over a century,” said senior author Lee Harrison, M.D., professor of infectious diseases at Pitt’s School of Medicine and epidemiology at Pitt’s Graduate School of Public...
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