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) identifier identifier identifier

  • 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.