A University of Arizona research team created a model that was able to successfully predict approximately how many asthma sufferers would visit the emergency room at a large hospital in Dallas on a given day, based on an analysis of data gleaned from electronic medical records, air quality sensors and Twitter.
Led by Sudha Ram, a University of Arizona professor of management information systems and computer science, and Dr. Yolande Pengetnze, a physician scientist at the Parkland Center for Clinical Innovation in Dallas, the researchers looked specifically at the chronic condition of asthma and how asthma-related tweets, analyzed alongside other data, can help predict asthma-related emergency room visits.
By analyzing tweets and air quality information together, Ram and her collaborators were able to use machine learning algorithms to predict with 75 percent accuracy whether the emergency room could expect a low, medium or high number of asthma-related visits on a given day.
The research highlights the important role that big data, including streams from social media and environmental sensors, could play in addressing health challenges, Ram said.
"You can get a lot of interesting insights from social media that you can't from electronic health records," Ram said. "You only go to the doctor once in a while, and you don't always tell your doctor how much you've been exercising or what you've been eating. But people share that information all the time on social media. We think that prediction models like this can be very useful, if we can combine various types of data, to address chronic diseases."
