Twitter can help predict emergency room visits

Researchers, led by an Indian-origin scientist, have analysed asthma-related tweets, along with data from air quality sensors, to successfully predict how many sufferers would visit the emergency room on a given day.

Washington: Researchers, led by an Indian-origin scientist, have analysed asthma-related tweets, along with data from air quality sensors, to successfully predict how many sufferers would visit the emergency room on a given day.

University of Arizona researchers said that Twitter users who post information about their personal health online might be considered by some to be "over-sharers," but health-related tweets may have the potential to be helpful for hospitals.

Led by Sudha Ram, a UA 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, analysed alongside other data, can help predict asthma-related emergency room visits.

Ram and her collaborators 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.

The findings could help hospital emergency departments in the US plan better with regard to staffing and resource management, said Ram, the paper's lead author.

"We realised 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," Ram said.

Over a three-month period, Ram and her team collected air quality data from environmental sensors in the vicinity of the Dallas hospital.

They also gathered and analysed 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 analysing tweets and air quality information together, researchers were able to use machine learning algorithms to predict with 75 per cent accuracy whether the emergency room could expect a low, medium or high number of asthma-related visits on a given day.

Ram hopes that the findings will help create similar predictive models for emergency room visits related to other chronic conditions, such as diabetes.  

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