Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance

TL;DR

This paper demonstrates that combining cloud-based electronic health records with machine learning and historical data can enable accurate, real-time regional influenza outbreak predictions, aiding public health decision-making.

Contribution

It introduces a novel approach integrating cloud EHR data, machine learning, and epidemiological history for real-time flu surveillance at regional levels.

Findings

Achieved accurate near real-time regional flu predictions.

Validated the approach using US epidemiological data.

Showed potential for public health decision support.

Abstract

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.