Using Twitter data to predict A&E visits in asthma

July 20, 2015 − by Suzanne Elvidge − in Big data, Data analytics, Data mining, Healthcare big data analysis − 1 Comments

A model using big data from asthma-related tweets could help doctors target treatments and hospitals plan and make better use of resources by predicting asthma-related accident & emergency (A&E; emergency room) visits. The results are being published in the IEEE Journal of Biomedical and Health Informatics.

Asthma is a common, incurable and chronic respiratory disease that can lead to just a few wheezes, or full-blown and even life-threatening asthma attacks that require hospitalisation.

“We realized that asthma is one of the biggest traffic generators in the emergency department,” says Sudha Ram, a professor of management information systems and computer science at the University of Arizona. “Often what happens is that there are not the right people in the emergency department to treat these patients, or not the right equipment, and that causes a lot of unforeseen problems.”

Over three months, the team of researchers collected millions of tweets from around the world that mentioned keywords linked with asthma, such as ‘asthma’, ‘wheezing’ or ‘inhaler’. They then used text mining to link the records with electronic medical records in the right geographic area, along with near real-time environmental data from an air quality sensor. This showed connections between rising levels of emergency asthma visits and worsening air quality or increases in numbers of asthma-related tweets.

They used the tweet and air quality data to create a model that can predict whether the number of asthma A&E visits on a given day would be low, medium or high, with around 75% accuracy. This could help plan staffing and resource management in hospitals. The numbers of asthma-related Google searches in the given area didn’t work as well for prediction. Similar techniques could be used to create similar predictive models for emergency room visits related to other chronic conditions, such as diabetes.

“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.”

“People often end up in the emergency room not necessarily for contagious diseases but for complications resulting from chronic conditions like asthma or diabetes or cardiac problems, which cost a lot to our health care system,” says Ram. “The CDC gets reports of emergency department visits several weeks after the fact, and then they put out surveillance maps. With our new model, we can now do this in almost real time, so that’s an important public health surveillance implication.”

The next step is to expand the asthma study to 75 hospitals in the Dallas-Fort Worth area.

“We’ve got really good results,” Ram said, “and now we’re working on building even more robust models to see if we can increase the accuracy level by using more types of datasets over a longer time period.”





One Comment

  1. excellent use case..

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