Predicting Asthma-Related Emergency Department Visits Using Big Data - 2015
Asthma is one in all the foremost prevalent and costly chronic conditions within the United States, which can't be cured. However, accurate and timely surveillance data might permit for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems will have knowledge availability lags of up to 2 weeks. Rapid progress has been created in gathering nontraditional, digital info to perform disease surveillance. We tend to introduce a unique methodology of using multiple information sources for predicting the amount of asthma-related emergency department (ED) visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. Our preliminary findings show that our model will predict the quantity of asthma ED visits based on close to-real-time environmental and social media data with approximately seventy% precision. The results will be helpful for public health surveillance, ED preparedness, and targeted patient interventions.
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