In this podcast Arjun Nathan discusses the paper:
Predicting intraoperative and postoperative consequential events using machine learning techniques in patients undergoing robotic partial nephrectomy (RPN):  Vattikuti Collective Quality Initiative (VCQI) database study
(https://bjui-journals.onlinelibrary.wiley.com/doi/10.1111/BJU.15087)

Arjun Nathan is an ST1 in Urology in North London, UK and NIHR Academic Clinical Fellow with the Royal College of Surgeons. He is also the BURST Treasurer and committee member.

BJUI/BURST: Machine Learning Partial Nephrectomy Complications

BJUI aims to provide the very highest standard of research and clinical information for the urological community, promoting awareness of new advances and supporting best practice in urology.

The journal publishes original articles, reviews, comments and educational articles. Clinical relevance is critical and the journal covers the entire breadth of urology under the following sections: Comments, Reviews, Urological Oncology (all areas of urological oncology as well as recent advances in diagnostic techniques), Robotics and Laparoscopy, Upper Urinary Tract, Functional Urology, Andrology, Translational Science and Professional Innovation.

BJUI is constantly innovating to provide the urology community with a journal that is fresh in appearance and easy to read.

BJUI/BURST: Machine Learning Partial Nephrectomy Complications

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