Predicting outcomes in kidney stone endoscopic surgery by rotation forest algorithm-Reference-Cited by-同舟云学术

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Predicting outcomes in kidney stone endoscopic surgery by rotation forest algorithm

Published:2022-10-12 Issue: Volume: Page:1-11
ISSN:2168-1163
Container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Author:

Pooyesh Shima¹,Foshati Saghar¹^ORCID,Sabeti Malihe²^ORCID,Parvin Hamid³,Aminsharifi Alireza⁴⁵

Affiliation:

1. Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

2. Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

3. Department of Computer Engineering, Nourabad Branch, Islamic Azad University, Noorabad, Iran

4. Department of Urology, Shiraz University of Medical Sciences, Shiraz, Iran

5. Department of Urology and Surgery, Pennsylvannia State University, Hershey, PA, USA

Publisher

Informa UK Limited

Subject

Computer Science Applications,Radiology, Nuclear Medicine and imaging,Biomedical Engineering,Computational Mechanics

Link

https://www.tandfonline.com/doi/pdf/10.1080/21681163.2022.2131629

Reference26 articles.

1. Percutaneous Nephrolithotomy Outcomes Based on S.T.O.N.E., GUY, CROES, and S-ReSC Scoring Systems: The First Prospective Study

2. Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Software Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram

3. Physiopathology and etiology of stone formation in the kidney and the urinary tract

4. A Prediction Model Using Machine Learning Algorithm for Assessing Stone-Free Status after Single Session Shock Wave Lithotripsy to Treat Ureteral Stones

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