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Machine learning model provides rapid prediction of C. difficile infection risk: Model successfully applied to data from medical centers with different patient populations, electronic health record systems

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"Despite substantial efforts to prevent C. difficile infection and to institute early treatment upon diagnosis, rates of infection continue to increase," says Erica Shenoy, MD, PhD, of the MGH Division of Infectious Diseases, co-senior author of the study and assistant professor of Medicine at Harvard Medical School. "We need better tools to identify the highest risk patients so that we can target both prevention and treatment interventions to reduce further transmission and improve patient outcomes." The authors note that most previous models of C. difficile infection risk were designed as "one size fits all" approaches and included only a few risk factors, which limited their usefulness. Co-lead authors Jeeheh Oh, a U-M graduate student in Computer Science and Engineering, and Maggie Makar, MS, of MIT's Computer Science and Artificial Intelligence Laboratory and their colleagues took a "big data" approach that analyzed the whole electronic health record (EHR) to predict a patient's C. difficile risk throughout the course of hospitalization. Their method allows the development of institution-specific models that could accommodate different patient populations, different EHR systems and factors specific to each institution.


Machine learning, EHR data helping to combat hospital infections

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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.


Predicting C. Diff Risk with Big Data and Machine Learning

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A new model analyzes a wealth of information to better predict which patients are more prone to the dangerous infection. Nearly 30,000 Americans die each year from an aggressive, gut-infecting bacteria called Clostridium difficile. Resistant to many common antibiotics, C. diff can flourish when antibiotic treatment kills off beneficial bacteria that normally keep the deadly infection at bay. But doctors often struggle to determine when to take preventive action. New machine learning models tailored to individual hospitals could offer a much earlier prediction of which patients are most likely to develop C. diff, potentially helping stave off infection before it starts.


Spotting C. diff Risks with 'Hospital-Specific Approach' to Big Data

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A team of medical researchers trying to predict which hospital patients face the highest risk of contracting Clostridium difficile (C. Perhaps the key to understanding C. diff risk factors is context, they suggested. So the team of researchers from the University of Michigan, Massachusetts General Hospital, and the Massachusetts Institute of Technology (MIT) devised a project to test whether risk factors vary from one facility to the next. Jenna Wiens, PhD, a senior author on the paper and an assistant professor of computer science and engineering at the University of Michigan in Ann Arbor, said the project threw out some of the overly generalized assumptions that inhibited past efforts to predict which patients would face the highest C. diff risk. "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," Wiens told the university's Michigan Health Lab publication.


Michigan Medicine makes AI, machine learning a top tech priority

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The academic medical center of the University of Michigan is leveraging investments in artificial intelligence, machine learning and advanced analytics to unlock the value of its health data. According to Andrew Rosenberg, MD, chief information officer for Michigan Medicine, the organization currently has 34 ongoing AI and machine leaning projects, 28 of which have principal investigators. "There's a lot of collaboration around these projects--as there should be for the diversity of thought and background needed to deal with complex problems--working with at least seven other U of M schools," Rosenberg told the Machine Learning for Health Care conference on Friday in Ann Arbor, Mich. "That's one of the powers that we enjoy." One of the machine learning projects cited by Rosenberg leverages a combination of electronic health records, monitor data and analytics to predict acute hemodynamic instability--when blood flow drops and deprives the body of oxygen--which is one of the most common causes of death for critically ill or injured patients.