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., ...Daha Fazla

BJU international, cilt.126, ss.350-358, 2020 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 126
Basım Tarihi: 2020
Doi Numarası: 10.1111/bju.15087
Dergi Adı: BJU international
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, BIOSIS, CAB Abstracts, EMBASE, Gender Studies Database, MEDLINE, Public Affairs Index
Sayfa Sayıları: ss.350-358
Anahtar Kelimeler: 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
Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Evet

Özet

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