“This grant was initially motivated by work that we were doing with healthcare data — and particularly electronic health records data,” said Honavar. “Think about every time you go to the hospital — let’s say, for a routine checkup — the doctors will probably record a range of physiological parameters, for example, they may take your blood pressure, or blood sugar, or heart rate, along with the results of other tests. Basically, this is your health inventory — a bunch of parameters — that define your health status at that point in time.”

The data could be used in different ways, such as predicting health risks for certain conditions, or watching how risk for health conditions change over time…. The machine learning techniques that the researchers are developing through the grant might be able to produce more accurate predictions by flexible modeling of longitudinal data, and by accurate learning of complex correlations in the massive amounts of data with large numbers of variables.

While the grant’s primary focus is developing advanced machine learning tools for predictive modeling of longitudinal data, and applying the resulting tools on healthcare data, [the principal investigator] expects the research could produce applications in other fields, such as education, social sciences, life sciences and economics.

Read more:

Swayne, M. (2022, October 11). Project aims to use artificial intelligence to turn health data into predictions. Penn State News. https://www.psu.edu/news/institute-computational-and-data-sciences/story/project-aims-use-artificial-intelligence-turn/