Predicting Asthma‐Related Emergency Department Visits Using Big Data

July 8, 2015

Predicting Asthma‐Related Emergency Department Visits Using Big Data

Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of leveraging the non-traditional, digital information to predict the volume of asthma‐related emergency room visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. The preliminary findings show that our model can predict the number of asthma ED visits based on near‐real‐time environmental and social media data with approximately 70% precision. The findings could help hospital emergency departments plan better with regard to staffing and equipment availability in a flexible manner. They can also provide early warning signals to the people at risk for asthma adverse events, and enable timely, proactive, and targeted preventive and therapeutic interventions.



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