Etminani Payam

The National Strategy for Biosurveillance promotes a national effort to improve early detection and enable ongoing situational awareness of all-hazards threats. Implicit in the Strategy’s implementation plan is the need to upgrade capabilities and integrate multiple disparate data sources, including more complete electronic health record (EHR) data into future biosurveillance capabilities. Thus, new biosurveillance applications are clearly needed. Praedico™ is a next generation biosurveillance application that incorporates cloud computing technology, a Big Data platform utilizing MongoDB as a data management system, machine-learning algorithms, geospatial and advanced graphical tools, multiple EHR domains, and customizable social media streaming from public health-related sources, all within a user friendly interface.

Objective

The purpose of our study was to conduct an initial assessment of the biosurveillance capabilities of a new software application called Praedico™ and compare results obtained from previous queries with the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE).

 

Many methods to detect outbreaks currently exist, although most are ineffective in the face of real data, resulting in high false positivity. More complicated methods have better precision, but can be difficult to interpret and justify. Praedico™ is a next generation biosurveillance application built on top of a Hadoop High Performance Cluster that incorporates multiple syndromic surveillance methods of alerting, and a machine-learning (ML) model using a decision tree classifier  evaluating over 100 different signals simultaneously, within a user friendly interface.

Objective

To compare syndromic surveillance alerting in VA using Praedico™ and ESSENCE.