  Accurate real-time monitoring systems of influenza outbreaks help public
health officials make informed decisions that may help save lives. We show that
information extracted from cloud-based electronic health records databases, in
combination with machine learning techniques and historical epidemiological
information, have the potential to accurately and reliably provide near
real-time regional predictions of flu outbreaks in the United States.
