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Machine-learning algorithm for predicting stone recurrence shows promise
“We were able to develop a machine learning model that had decent accuracy…in predicting which patients would have an additional stone event and which patients wouldn't,” says Kevin Shee, MD, PhD.
In this video, Kevin Shee, MD, PhD, discusses the background and notable findings of the study, “A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-hour Urine Data,” which was presented at the Western Section of the American Urological Association Annual Meeting in Koloa, Hawaii. Shee is a urology resident at the University of California, San Francisco.
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