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TUCSON, AZ – Believe it or not, some people suffering an acute asthma attack take the time to tweet about their experiences.
As bizarre as that level of oversharing might seem, it actually benefits emergency departments, according to a new study in the IEEE Journal of Biomedical and Health Informatics' special issue on big data. University of Arizona researchers found that Twitter data coupled with air quality sensors can help predict ED traffic.
The researchers, led by Sudha Ram, PhD, a UA professor of management information systems and computer science, created a model that was able to use asthma-related tweets along with electronic medical record information and data from air quality sensors to successfully predict approximately how many asthma sufferers would visit the ED at a large hospital in Dallas on a given day.
"We realized that asthma is one of the biggest traffic generators in the emergency department," Ram said. "Often what happens is that there are not the right people in the ED to treat these patients, or not the right equipment, and that causes a lot of unforeseen problems."
The study team, including researchers from the Parkland Center for Clinical Innovation in Dallas, collected air quality data from environmental sensors in the vicinity of the hospital while simultaneously gathering and analyzing tweets containing certain keywords such as "asthma," "inhaler" or "wheezing." Text-mining techniques then were used to drill down to relevant tweets in the ZIP codes detailing where most of the hospital's patients live, as determined by electronic medical records.
As certain air quality measures worsened, asthma visits to the ED went up, the researchers determined. Asthma visits also increased as the number of asthma-related tweets went up. While researchers also looked at asthma-related Google searches in the area, they found those were not a good predictor for ED visits.
The researchers employed machine learning algorithms to predict with 75% accuracy whether the emergency room could expect a low, medium or high number of asthma-related visits on a specific day.
"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."