Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance
This addresses the need for timely outbreak monitoring to aid public health officials in decision-making, representing an incremental improvement by applying existing methods to new data sources.
The paper tackled the problem of real-time influenza surveillance by using cloud-based electronic health records with machine learning and historical data, achieving accurate and reliable near real-time regional predictions of flu outbreaks in the U.S.
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.