“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.
Destigmatizing Urology: Dr. Pruthi discusses mental health
May 23rd 2022“I think we often encourage our patients to share concerns about depression, anxiety, and other mental health conditions, yet are less likely ourselves to seek help due to this issue of stigma,” says Raj S. Pruthi, MD, MHA, FACS.
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