How Twitter Can Help Predict Emergency Room Visits

How Twitter Can Help Predict Emergency Room Visits
Researchers tracked millions of asthma-related tweets around
the world, shown in this visualization, then zoomed in on a
particular region to see how the tweets, when analyzed
alongside other data, could help predict asthma-related
 emergency room visits. (Credit: University of Arizona)
Twitter users who post information about their personal health online might be considered by some to be "over-sharers", but new research suggests that health-related tweets may have the potential to be helpful for hospitals. 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.
 
They 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.
 
Their findings, to be published in the forthcoming IEEE Journal of Biomedical and Health Informatics' special issue on big data, could help hospital emergency departments nationwide plan better with regard to staffing and resource management.
 
Over a three-month period, the researchers collected air quality data from environmental sensors in the vicinity of the Dallas hospital. They also gathered and analyzed asthma-related tweets containing certain keywords such as "asthma," "inhaler" or "wheezing." After collecting millions of tweets from across the globe, they used text-mining techniques to zoom in on relevant tweets in the ZIP codes where most of the hospital's patients live, according to electronic medical records.
 
The researchers found that as certain air quality measures worsened, asthma visits to the emergency room went up. Asthma visits also increased as the number of asthma-related tweets went up. The researchers additionally looked at asthma-related Google searches in the area but found that they were not a good predictor for asthma emergency room visits.
 
By analyzing tweets and air quality information together, the researchers 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. The researchers also hope that their findings will help them create similar predictive models for emergency room visits related to other chronic conditions, such as diabetes.
 
Based on material originally posted by University of Arizona.