Machine Learning Outperform Traditional Approaches in Predicting Clinically Significant Prostate Cancer

Ordones, Flavio Vasconcelos; Vermeulen, Lodewikus; Hooshyari, Ali; Scholtz, David; Kawano, Paulo; de Andrade, Gustavo Modelli; Barros, Abner; Gilling, Peter

dc.contributor.author	Ordones, Flavio Vasconcelos
dc.contributor.author	Vermeulen, Lodewikus
dc.contributor.author	Hooshyari, Ali
dc.contributor.author	Scholtz, David
dc.contributor.author	Kawano, Paulo
dc.contributor.author	de Andrade, Gustavo Modelli
dc.contributor.author	Barros, Abner
dc.contributor.author	Gilling, Peter
dc.date.accessioned	2024-06-10T23:28:52Z
dc.date.available	2024-06-10T23:28:52Z
dc.date.issued	2024-05
dc.identifier.citation	(2024). AUA Annual Meeting 2024, San Antonio, TX, USA, 03 May 2024 - 06 May 2024. Journal of Urology. Lippincott, Williams & Wilkins. 211: e503-e503. May 2024
dc.identifier.issn	0021-0005
dc.identifier.uri	https://hdl.handle.net/2292/68773
dc.language	en
dc.publisher	Wolters Kluwer
dc.relation.ispartofseries	Journal of Urology
dc.rights	Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri	https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.subject	32 Biomedical and Clinical Sciences
dc.subject	3211 Oncology and Carcinogenesis
dc.title	Machine Learning Outperform Traditional Approaches in Predicting Clinically Significant Prostate Cancer
dc.type	Conference
dc.identifier.doi	10.1097/01.ju.0001008936.35187.0b.02
pubs.issue	5S
pubs.begin-page	e503
pubs.volume	211
dc.date.updated	2024-05-13T08:14:52Z
dc.rights.holder	Copyright: American Urological Association Education and Research, Inc.	en
pubs.publication-status	Published
dc.rights.accessrights	http://purl.org/eprint/accessRights/RetrictedAccess	en
pubs.subtype	Abstract
pubs.elements-id	1026889
pubs.org-id	Medical and Health Sciences
pubs.org-id	School of Medicine
pubs.org-id	Surgery Department
dc.identifier.eissn	1527-3792
pubs.record-created-at-source-date	2024-05-13