Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study-Reference-Cited by-同舟云学术

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Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study

Published:2020-05-18 Issue:3 Volume:126 Page:350-358
ISSN:1464-4096
Container-title:BJU International
language:en
Short-container-title:BJU Int

Author:

Bhandari Mahendra¹^ORCID,Nallabasannagari Anubhav Reddy²^ORCID,Reddiboina Madhu²^ORCID,Porter James R.³^ORCID,Jeong Wooju¹,Mottrie Alexandre⁴,Dasgupta Prokar⁵,Challacombe Ben⁶,Abaza Ronney⁷,Rha Koon Ho⁸^ORCID,Parekh Dipen J.⁹,Ahlawat Rajesh¹⁰,Capitanio Umberto¹¹^ORCID,Yuvaraja Thyavihally B.¹²,Rawal Sudhir¹³,Moon Daniel A.¹⁴^ORCID,Buffi Nicolò M.¹⁵,Sivaraman Ananthakrishnan¹⁶,Maes Kris K.¹⁷,Porpiglia Francesco¹⁸^ORCID,Gautam Gagan¹⁹^ORCID,Turkeri Levent²⁰,Meyyazhgan Kohul Raj²,Patil Preethi²¹,Menon Mani¹,Rogers Craig¹^ORCID

Affiliation:

1. Vattikuti Urology Institute; Henry Ford Hospital; Detroit MI USA

2. RediMinds Inc.; Southfield MI USA

3. Swedish Medical Centre; Seattle WA USA

4. OLV Vattikuti Institute; Aalst Belgium

5. MRC Centre of Transplantation; King’s College London; London UK

6. Guy's and St Thomas' Hospitals; London UK

7. Ohio Health Dublin Methodist Hospital; Dublin OH USA

8. Yonsei University; Seoul Korea

9. Sylvester Comprehensive Cancer Centre; University of Miami Miller School of Medicine; Miami FL USA

10. Medanta Vattikuti Institute; Medanta - The Medicity; Gurugram Haryana India

11. Urology Clinic; San Raffaele Hospital; Milan Italy

12. Kokilaben Dhirubhai Ambani Hospital; Mumbai India

13. Rajiv Gandhi Cancer Institute and Research Centre; New Delhi India

14. Peter MacCallum Cancer Centre; Melbourne Vic. Australia

15. Humanitas Research Hospital; Milan Italy

16. Apollo Hospitals; Chennai India

17. Centre for Robotic and Minimally Invasive Surgery; Hospital Da Luz; Luz Sáude Portugal

18. San Luigi Gonzaga Hospital; University of Turin; Orbassano Italy

19. Max Institute of Cancer Care; Saket India

20. Acıbadem University School of Medicine; Istanbul Turkey

21. Vattikuti Foundation; Southfield MI USA

Funder

RediMinds Inc.

Publisher

Wiley

Subject

Urology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/bju.15087/fullpdf

Reference41 articles.

1. Outcomes of robot-assisted partial nephrectomy for clinical T2 renal tumors: a multicenter analysis (ROSULA Collaborative Group);Bertolo;Eur Urol,2018

2. Perioperative complications of robot-assisted partial nephrectomy: analysis of 886 patients at 5 United States centers;Tanagho;Urology,2013

3. Predictors of warm ischemia time and perioperative complications in a multicenter, international series of robot-assisted partial nephrectomy;Ficarra;Eur Urol,2012

4. Predicting morbidity after robotic partial nephrectomy: the effect of tumor, environment, and patient-related factors;Khene;Urol Oncol,2018

5. The impact of surgeon volume on perioperative outcomes and cost for patients receiving robotic partial nephrectomy;Khandwala;J Endourol,2017

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3. Current advances in the use of artificial intelligence in predicting and managing urological complications;International Urology and Nephrology;2024-07-09

4. Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study;Surgical Endoscopy;2023-10-14

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