From: University of Calgary
Published April 19, 2017 06:38 AM

Which patients face highest risk for ending up in emergency rooms?

Biostatistician Colin Weaver takes the same kind of algorithms and machine learning techniques that land a certain blender on your Amazon page or a suggested movie in your Netflix account, and he applies them to something slightly more consequential: health care.

A master’s student in community health, Weaver’s work combines his expertise with statistics and a long-standing interest in improving health care. For his current research, he will use data and machine learning techniques to predict which patients are most likely to end up in the emergency department based on patterns in, for example, doctor visits, diagnoses, or prescription combinations.

“There are emergency visits that are unavoidable, but the visits we are interested in predicting are the ones that could perhaps be pre-empted or headed off, generally thought to be visits resulting from poorly managed chronic conditions,” Weaver says.

“If we can find which health care events increase someone’s risk of ending up in emergency, we will be able to identify patients early and hopefully provide them with some additional care to prevent their emergency visit.”


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