Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry Gupta, Sunil, Tran, Truyen, Luo, Wei, Phung, Dinh, Kennedy, Richard Lee, Broad, Adam, Campbell, David, Kipp, David, Singh, Madhu, Khasraw, Mustafa, Matheson, Leigh, Ashley, David M. and Venkatesh, Svetha 2014, Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry, BMJ open, vol. 4, no. 3, Article number e004007, pp. 1-7, doi: 10.1136/bmjopen-2013-004007. Attached Files Name Description MIMEType Size Downloads gupta-machinelearning-2014.pdf Published version application/pdf 702.49KB 161 Title Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry Author(s) Gupta, Sunil orcid.org/0000-0002-3308-1930 Tran, Truyen orcid.org/0000-0001-6531-8907 Luo, Wei orcid.org/0000-0002-4711-7543 Phung, Dinh orcid.org/0000-0002-9977-8247 Kennedy, Richard Lee Broad, Adam Campbell, David Kipp, David Singh, Madhu Khasraw, Mustafa orcid.org/0000-0003-3249-9849 Matheson, Leigh Ashley, David M. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Journal name BMJ open Volume number 4 Issue number 3 Season Article number e004007 Start page 1 End page 7 Total pages 7 Publisher B M J Group Place of publication London, England Publication date 2014 ISSN 2044-6055 Keyword(s) Cancer Electronic Medical Record Machine Learning Prediction Survival Science & Technology Life Sciences & Biomedicine Medicine, General & Internal General & Internal Medicine POPULATION-BASED COHORT BREAST-CANCER IMPACT COMORBIDITY NETWORKS HEALTH MODELS Summary Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes. Language eng DOI 10.1136/bmjopen-2013-004007 Field of Research 111299 Oncology and Carcinogenesis not elsewhere classified Socio Economic Objective 920102 Cancer and Related Disorders HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2014, BMJ Group Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30067403 Document type: Journal Article Collections: Faculty of Health School of Medicine Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 14 Nov 2014, 09:11:14 EST Mon, 08 Dec 2014, 13:59:29 EST Mon, 08 Dec 2014, 15:09:25 EST Mon, 08 Dec 2014, 15:21:01 EST Wed, 17 Dec 2014, 10:29:48 EST Wed, 17 Dec 2014, 10:31:08 EST Fri, 19 Dec 2014, 11:01:48 EST Wed, 14 Oct 2015, 13:56:32 EST Tue, 14 Jun 2016, 08:59:00 EST Tue, 14 Jun 2016, 09:57:52 EST Wed, 07 Sep 2016, 07:56:40 EST Wed, 06 Dec 2017, 06:10:49 EST Fri, 07 Sep 2018, 17:20:51 EST Filtered Full Citation counts:   Cited 34 times in TR Web of Science Cited 44 times in Scopus Search Google Scholar Access Statistics: 776 Abstract Views, 162 File Downloads  -  Detailed Statistics Created: Mon, 08 Dec 2014, 13:59:29 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.