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DADAR: A New Situational Awareness Platform for Data Analysis, Detection and Response

Description

We previously experimented with tracking influenza in ER chief complaint data using existing syndromic surveillance tools. We identified several deficiencies in these tools: poor natural language processing, inefficient user interfaces, frequent (thus costly) false alarms, and one-size-fits-all approaches to syndromes. Furthermore, we were surprised that some epidemiologists we spoke with had relatively little faith in existing surveillance tools, and so we set out to build one that would address their concerns: DADAR (Data Analysis, Detection, And Response).

Objective

To develop an adaptable platform for periodically loading semi-structured medical text, extracting syndromic information using advanced natural language processing, detecting outbreaks in the data (including the ability to tune sensitivity vs. specificity on a syndrome-by-syndrome basis so as to reduce the rate of false alarms), generating timely cartographic surveillance reports, and providing tools to quickly validate or rule out syndromic alerts.

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