In Pursuit of Better Data Forecasting
Yao Xie is aiming to create higher confidence in the data models that drive societal outcomes
By David Mitchell
In countless fields from law enforcement to medicine and many in between, professionals look to make educated predictions about outcomes based on available data.
Consider police who may want to find two crimes committed by the same person. Can they find the needle in the proverbial haystack of countless police reports? How about a doctor trying to make decisions that will prevent patients from encountering sepsis? Can they parse enough data – blood pressure, blood sugar, temperatures, heart rate, and more – at various times to come up with a prediction with high enough confidence levels to help inform a doctor’s potentially life-and-death decisions?
Current approaches typically rely on statistical models, but those are often simplified to avoid plummeting confidence levels. A new study by researchers in Georgia Tech’s H. Milton Stewart School of Industrial Systems and Engineering (ISyE), and led by Yao Xie, associate professor in the school, offers a new approach using deep learning that could change the way decisions are made in similar situations.