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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.

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Title Understanding toxicities and complications of cancer treatment: a data mining approach
Author(s) Nguyen, Dang
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for 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
Collection: Centre for Pattern Recognition and Data Analytics
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