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The Tipping Point for Deep Learning in Oncology

The Tipping Point for Deep Learning in Oncology Deep Natural Language Processing to Ascertain Oncologic Outcomes From Radiology Reports at Scale Original Investigation Research Cooperative Oncology Groups. Clin Cancer Res. 38. Stead WW. Clinical implications and challenges a multi-center study. J Am Med Inform Assoc. 2018; 2012;18(1):256-262. doi:10.1158/1078-0432.CCR-11- of artificial intelligence and deep learning. JAMA. 25(9):1197-1201. doi:10.1093/jamia/ocy088 1633 2018;320(11):1107-1108. doi:10.1001/jama.2018.11029 42. Li X, Fireman BH, Curtis JR, et al. 35. Nasukawa T, Yi J. Sentiment analysis. In: 39. Pan SJ, Yang Q. A survey on transfer learning. Privacy-protecting analytical methods using only Proceedings of the International Conference on IEEE Trans Knowl Data Eng. 2010;22:1345-1359. aggregate-level information to conduct Knowledge Capture—K-CAP ’03. New York, doi:10.1109/TKDE.2009.191 multivariable-adjusted analysis in distributed data New York: ACM Press; 2003:70. networks. Am J Epidemiol. 2019;188(4):709-723. 40. Dayhoff JE, DeLeo JM. Artificial neural doi:10.1093/aje/kwy265 36. Mullard A. Learning from exceptional drug networks: opening the black box. Cancer. 2001;91 responders. Nat Rev Drug Discov. 2014;13(6):401- (8)(suppl):1615-1635. doi:10.1002/1097-0142 402. doi:10.1038/nrd4338 (20010415)91:8+<1615::AID-CNCR1175>3.0.CO;2-L 37. Hinton G. Deep learning—a technology with the 41. Ratwani RM, Savage E, Will A, et al. A usability potential to transform health care. JAMA. 2018;320 and safety analysis of electronic health records: (11):1101-1102. doi:10.1001/jama.2018.11100 Invited Commentary Andrew Daniel Trister, MD, PhD Approaches to artificial http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Oncology American Medical Association

The Tipping Point for Deep Learning in Oncology

JAMA Oncology , Volume 5 (10) – Oct 25, 2019

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References (2)

Publisher
American Medical Association
Copyright
Copyright 2019 American Medical Association. All Rights Reserved.
ISSN
2374-2437
eISSN
2374-2445
DOI
10.1001/jamaoncol.2019.1799
Publisher site
See Article on Publisher Site

Abstract

Deep Natural Language Processing to Ascertain Oncologic Outcomes From Radiology Reports at Scale Original Investigation Research Cooperative Oncology Groups. Clin Cancer Res. 38. Stead WW. Clinical implications and challenges a multi-center study. J Am Med Inform Assoc. 2018; 2012;18(1):256-262. doi:10.1158/1078-0432.CCR-11- of artificial intelligence and deep learning. JAMA. 25(9):1197-1201. doi:10.1093/jamia/ocy088 1633 2018;320(11):1107-1108. doi:10.1001/jama.2018.11029 42. Li X, Fireman BH, Curtis JR, et al. 35. Nasukawa T, Yi J. Sentiment analysis. In: 39. Pan SJ, Yang Q. A survey on transfer learning. Privacy-protecting analytical methods using only Proceedings of the International Conference on IEEE Trans Knowl Data Eng. 2010;22:1345-1359. aggregate-level information to conduct Knowledge Capture—K-CAP ’03. New York, doi:10.1109/TKDE.2009.191 multivariable-adjusted analysis in distributed data New York: ACM Press; 2003:70. networks. Am J Epidemiol. 2019;188(4):709-723. 40. Dayhoff JE, DeLeo JM. Artificial neural doi:10.1093/aje/kwy265 36. Mullard A. Learning from exceptional drug networks: opening the black box. Cancer. 2001;91 responders. Nat Rev Drug Discov. 2014;13(6):401- (8)(suppl):1615-1635. doi:10.1002/1097-0142 402. doi:10.1038/nrd4338 (20010415)91:8+<1615::AID-CNCR1175>3.0.CO;2-L 37. Hinton G. Deep learning—a technology with the 41. Ratwani RM, Savage E, Will A, et al. A usability potential to transform health care. JAMA. 2018;320 and safety analysis of electronic health records: (11):1101-1102. doi:10.1001/jama.2018.11100 Invited Commentary Andrew Daniel Trister, MD, PhD Approaches to artificial

Journal

JAMA OncologyAmerican Medical Association

Published: Oct 25, 2019

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