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Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.

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Title: Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.
Authors: Kose MG; Department of Urology, Gaziosmanpasa Training and Research Hospital, Karayolları Mah Osmanbey Cad 621. Sok 34255, Gaziosmanpasa, Istanbul, Turkey. vensyou@gmail.com., Altay D; Department of Urology, Gaziosmanpasa Training and Research Hospital, Karayolları Mah Osmanbey Cad 621. Sok 34255, Gaziosmanpasa, Istanbul, Turkey., Guler A; Department of Urology, Gaziosmanpasa Training and Research Hospital, Karayolları Mah Osmanbey Cad 621. Sok 34255, Gaziosmanpasa, Istanbul, Turkey., Kardas S; Department of Urology, Gaziosmanpasa Training and Research Hospital, Karayolları Mah Osmanbey Cad 621. Sok 34255, Gaziosmanpasa, Istanbul, Turkey., Taskin V; Department of Urology, Gaziosmanpasa Training and Research Hospital, Karayolları Mah Osmanbey Cad 621. Sok 34255, Gaziosmanpasa, Istanbul, Turkey., Arslan B; Department of Urology, Gaziosmanpasa Training and Research Hospital, Karayolları Mah Osmanbey Cad 621. Sok 34255, Gaziosmanpasa, Istanbul, Turkey., Ozdemir E; Department of Urology, Gaziosmanpasa Training and Research Hospital, Karayolları Mah Osmanbey Cad 621. Sok 34255, Gaziosmanpasa, Istanbul, Turkey.
Source: International urology and nephrology [Int Urol Nephrol] 2026 Mar 25. Date of Electronic Publication: 2026 Mar 25.
Publication Type: Journal Article
Journal Info: Publisher: Springer Country of Publication: Netherlands NLM ID: 0262521 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2584 (Electronic) Linking ISSN: 03011623 NLM ISO Abbreviation: Int Urol Nephrol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1573-2584
DOI:10.1007/s11255-026-05107-2