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A retrospective cohort study on the use of machine learning to predict stone-free status following percutaneous nephrolithotomy: An experience from Saudi Arabia.
Alghafees, Mohammad A; Abdul Rab, Saleha; Aljurayyad, Abdulaziz S; Alotaibi, Tariq S; Sabbah, Belal Nedal; Seyam, Raouf M; Aldosari, Lama H; Alomar, Mohammad A.
Affiliation
  • Alghafees MA; College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
  • Abdul Rab S; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
  • Aljurayyad AS; Department of Urology, King Saud University Medical City, Riyadh, Saudi Arabia.
  • Alotaibi TS; Department of Urology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
  • Sabbah BN; College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
  • Seyam RM; Department of Urology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
  • Aldosari LH; Department of Urology, King Fahad University Hospital, Al-Khobar, Saudi Arabia.
  • Alomar MA; Department of Urology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
Ann Med Surg (Lond) ; 84: 104957, 2022 Dec.
Article in En | MEDLINE | ID: mdl-36536733
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Ann Med Surg (Lond) Year: 2022 Document type: Article Affiliation country: Saudi Arabia
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Clinical_trials / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Ann Med Surg (Lond) Year: 2022 Document type: Article Affiliation country: Saudi Arabia