How An Algorithim Caught The Ebola Outbreak

Science: this is why we keep you around:

Nine days before the World Health Organization announced the African Ebola outbreak now making headlines, an algorithm had already spotted it. HealthMap, a data-driven mapping tool developed out of Boston Children’s Hospital, detected a “mystery hemorrhagic fever” after mining thousands of web-based data sources for clues.

“We’ve been operating HealthMap for over eight years now,” says cofounder Clark Freifeld. “One of the main things that has allowed it to flourish is the availability of large amounts of public event data being accessible on the Internet.”

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As anyone who’s ever looked at the Internet knows, any bulk consumption of web content is bound to scoop up tons of noise, especially when sources like Twitter and blogs are involved. To cope with this, HealthMap applies a machine learning algorithm to filter out irrelevant information like posts about “Bieber fever” or uses of terms like “infection” and “outbreak” that don’t pertain to actual public health events.

“The algorithm actually looks at hundreds of thousands of example articles that have been labeled by our analysts and uses the examples to pick up on key words and phrases that tend to be associated with actual outbreak reports,” explains Freifeld. “The algorithm is continually improving, learning from our analysts through a feedback loop.”

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