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Study: EHR Data, Machine Learning Techniques Can Provide Real-Time Flu Surveillance

May 13, 2016

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Data extracted from cloud-based electronic health record (EHR) systems in combination with a machine learning algorithm can provide near real-time regional estimates of flu outbreaks, according to a study published in Nature Scientific Reports.

Researchers Boston Children’s Hospital’s Computational Health Informatics Program, Harvard Medical School and Harvard School of Engineering and Applied Sciences examined whether EHR data collected and distributed in near real-time by an electronic health records and cloud services company, athenahealth, combined with historical patterns of flu activity using a suitable machine learning algorithm, could accurately track real-time influenza activity (as reported by the U.S. Centers for Disease Control and Prevention, CDC), at the regional scale in the United States.

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