AI combined with EHR and other data improves influenza forecasting

With influenza cases elevated nationally and widespread throughout the country, researchers led by Boston Children's Hospital contend that machine learning can produce highly accurate local flu surveillance. In fact, they say that combining two forecasting methods with artificial intelligence produces the most accurate estimates of flu activity available to date--a week ahead of traditional healthcare-based reports, at the state level across the United States. While the Centers for Disease Control and Prevention monitors influenza-like illnesses (ILI) in the U.S. by gathering information from physicians' reports about patients with ILI seeking medical attention, the availability of the data has a lag time of as much as two weeks. However, in a study published on Friday in Nature Communications, researchers say they have successfully combined Google search frequencies and electronic health record data with spatio-temporal trends in influenza activity to produce forecasts with higher correlation and lower errors than all other tested models for current ILI activity at the state level. "We believe that the accuracy of our method involves a balance between responsiveness and robustness," state the authors.