Machine learning, EHR data helping to combat hospital infections

Hospitals continue to grapple with clostridium difficile infections, caused by bacteria that are resistant to many common antibiotics and that kill about 30,000 Americans each year. However, machine learning can help predict patient risk in developing C. difficile much earlier than current methods of diagnosis. Using electronic health records for nearly 257,000 patients, researchers from Massachusetts General Hospital, MIT and Michigan Medicine have built hospital-tailored machine learning models that they contend are an improvement over a "one-size-fits-all" approach that ignores important factors specific to medical facilities. "When data are simply pooled into a one-size-fits-all model, institutional differences in patient populations, hospital layouts, testing and treatment protocols, or even in the way staff interact with the EHR can lead to differences in the underlying data distributions and ultimately to poor performance of such a model," says Jenna Wiens, assistant professor of computer science and engineering at U-M. "To mitigate these issues, we take a hospital-specific approach, training a model tailored to each institution." De-identified EHR data from 191,014 adult admissions to Michigan Medicine and 65,718 adult admissions to MGH were analyzed using separate machine learning algorithms tailored to each healthcare institution with different types of variables. "These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies," conclude the authors in an article appearing in the April issue of the journal Infection Control and Hospital Epidemiology.