University of Florida researchers use artificial intelligence to better predict surgical outcomes

University of Florida researchers use artificial intelligence to better predict surgical outcomes


I work in a surgical intensive care unit
as an intensivist and nephrologist, and I take care of the patients who developed
unexpected complications that are unrelated to their surgery. About 150,000 patients a year in America dies from this complication of major surgery. So idea was to develop
autonomous intelligence system that can take all
the data in real time, process it, and then predict tricks or complications; not
just one time but on multiple occasions. I put together a team of both
physicians; in surgery anesthesiology, and then in computer science and IT, Health
IT to see whether we can find a better way to recognize which patients are at
risk for developing these complications before they go for surgery and
immediately afterwards. This is a machine learning algorithm,
so it trains as we go. So every three months we collect the data that we have done, all the
prediction, and we retrain the algorithm improve it and put it back in the system. We found out that when you just
look at a physicians forecasting, it’s probably in a zone of fifty to sixty percent, almost like a flip of coin. Compared to physician, the algorithm was better. I think that was not much of a surprise. We allowed physicians to review the algorithm output and physicians were asked to
recast their risk. Once they were able to review algorithm, and after they recast
their risk they were actually more precise. It’s not going to replace
physicians but it’s going to help them use the depth of data that exists in
electronic health record and what specific intervention to prevent those
complications that you are at high risk for,
need to be implemented. I think the dynamic
component and the ability to transfer the knowledge between different team of
physicians is probably the biggest use of this algorithm.

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