Understanding toxicities and complications of cancer treatment: a data mining approach Nguyen, Dang, Luo, Wei, Phung, Dinh and Venkatesh, Svetha 2015, Understanding toxicities and complications of cancer treatment: a data mining approach. In Pfahringer, Bernhard and Renz, Jochen (ed), AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings, Springer, Berlin, Germany, pp.431-443, doi: 10.1007/978-3-319-26350-2_38. Attached Files Name Description MIMEType Size Downloads Title Understanding toxicities and complications of cancer treatment: a data mining approach Author(s) Nguyen, Dang orcid.org/0000-0002-0401-988X Luo, Wei orcid.org/0000-0002-4711-7543 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Title of book AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings Editor(s) Pfahringer, Bernhard Renz, Jochen Publication date 2015 Series Lecture notes in computer science; v.9457 Chapter number 38 Total chapters 57 Start page 431 End page 443 Total pages 13 Publisher Springer Place of Publication Berlin, Germany Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Robotics Computer Science ASSOCIATION RULES COMORBIDITY Summary Cancer remains a major challenge in modern medicine. Increasing prevalence of cancer, particularly in developing countries, demands better understanding of the effectiveness and adverse consequences of different cancer treatment regimes in real patient population. Current understanding of cancer treatment toxicities is often derived from either “clean” patient cohorts or coarse population statistics. It is difficult to get up-to-date and local assessment of treatment toxicities for specific cancer centres. In this paper, we applied an Apriori-based method for discovering toxicity progression patterns in the form of temporal association rules. Our experiments show the effectiveness of the proposed method in discovering major toxicity patterns in comparison with the pairwise association analysis. Our method is applicable for most cancer centres with even rudimentary electronic medical records and has the potential to provide real-time surveillance and quality assurance in cancer care. ISBN 9783319263502 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-26350-2_38 Field of Research 080109 Pattern Recognition and Data Mining 08 Information And Computing Sciences Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2015, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30081382 Document type: Book Chapter Collections: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) 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. Versions Version Filter Type Mon, 15 Feb 2016, 16:09:48 EST Mon, 15 Feb 2016, 16:10:38 EST Tue, 16 Feb 2016, 04:01:29 EST Tue, 16 Feb 2016, 05:10:06 EST Tue, 12 Apr 2016, 10:26:52 EST Tue, 12 Apr 2016, 10:28:28 EST Tue, 12 Apr 2016, 10:31:11 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 610 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 15 Feb 2016, 16:10:38 EST

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