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.

Bhandari, Mahendra; Nallabasannagari, Anubhav; Reddiboina, Madhu; Porter, James; Jeong, Wooju; Mottrie, Alexandre; Dasgupta, Prokar; Challacombe, Ben; Abaza, Ronney; Rha, Koon; Parekh, Dipen; Ahlawat, Rajesh; Capitanio, Umberto; Yuvaraja, Thyavihally; Rawal, Sudhir; Moon, Daniel; Buffi, Nicolo; Sivaraman, Ananthakrishnan; Maes, Kris; Porpiglia, Francesco; Gautam, Gagan; Turkeri, LEVENT; Meyyazhgan, Kohul; Patil, Preethi; Menon, Mani; Rogers, Craig

doi:10.1111/bju.15087

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.

Bhandari M., Nallabasannagari A. R. , Reddiboina M., Porter J. R. , Jeong W., Mottrie A., ...More

BJU international, vol.126, pp.350-358, 2020 (Journal Indexed in SCI Expanded)

Publication Type: Article / Article
Volume: 126
Publication Date: 2020
Doi Number: 10.1111/bju.15087
Title of Journal : BJU international
Page Numbers: pp.350-358
Keywords: deep learning, intra-operative complications, machine learning, postoperative complications, postoperative morbidity, robot-assisted partial nephrectomy, LENGTH-OF-STAY, TUMOR SCORING SYSTEMS, PERIOPERATIVE COMPLICATIONS, ARTIFICIAL-INTELLIGENCE, RACIAL DISPARITIES, HOSPITAL VOLUME, IMPACT

Abstract

Objective To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery.