Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study.

ABSTRACT:

Background

Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users' activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data.

Objective

Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time.

Methods

We used Google data for internet data and the clinical data warehouse eHO

SUBMITTER: Poirier C 

PROVIDER: S-EPMC6320394 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature