Application of Machine Learning to Predict Symptomatic Recurrence Events for Patients With Kidney Stone

ML models can effectively discriminate between high and low risk of urinary stone recurrence events.

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Publication Details

Journal
Urology Practice
Published
2025-09-17
DOI
https://doi.org/10.1097/upj.0000000000000897
Primary Topic
Kidney Stones and Urolithiasis Treatments
Type
article
Field-Weighted Citation Impact
0.00
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article

Application of Machine Learning to Predict Symptomatic Recurrence Events for Patients With Kidney Stone

Reza Z. Goharderakhshan, Nikhil Crain, Drew Clausen, Ronald K. Loo et al.
Urology Practice
Kidney Stones and Urolithiasis Treatments
article

Application of Machine Learning to Predict Symptomatic Recurrence Events for Patients With Kidney Stone

Reza Z. Goharderakhshan, Nikhil Crain, Drew Clausen, Ronald K. Loo, Douglas A. Murad
article en

Abstract

ML models can effectively discriminate between high and low risk of urinary stone recurrence events.

Urology PracticeVol. 13(1)
Colorado Permanente Medical Group (US)
Openalex Percentile: Top 64%
Kidney Stones and Urolithiasis Treatments
DOISourceOpenAlex
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Application of Machine Learning to Predict Symptomatic Recurrence Events for Patients With Kidney Stone — Reza Z. Goharderakhshan, Nikhil Crain, et al. · Urology Practice (2025) | TGRS Research Map | TGRS