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Understanding toxicities and complications of cancer treatment: a data mining approach
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posted on 2015-01-01, 00:00 authored by Dang Pham Hai Nguyen, Wei LuoWei Luo, Svetha VenkateshSvetha Venkatesh, Quoc-Dinh PhungCancer 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.
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Title of book
AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 ProceedingsVolume
9457Series
Lecture notes in computer science; v.9457Chapter number
38Pagination
431 - 443Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319263502Language
engPublication classification
B1 Book chapter; B Book chapterCopyright notice
2015, SpringerExtent
57Editor/Contributor(s)
B Pfahringer, J RenzUsage metrics
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