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Machine-learning algorithm for predicting stone recurrence shows promise
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“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.
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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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