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Toxicity prediction in cancer using multiple instance learning in a multi-task framework

Li, Cheng, Gupta, Sunil, Rana, Santu, Luo, Wei, Venkatesh, Svetha, Ashley, David and Phung, Dinh 2016, Toxicity prediction in cancer using multiple instance learning in a multi-task framework. In Bailey, James, Khan, Latifur, Washio, Takashi, Dobbie, Gillian, Huang, Joshua Zhexue and Wang, Ruili (ed), Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Springer, Berlin, Germany, pp.152-164, doi: 10.1007/978-3-319-31753-3_9.

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Title Toxicity prediction in cancer using multiple instance learning in a multi-task framework
Author(s) Li, Cheng
Gupta, Sunil
Rana, Santu
Luo, Wei
Venkatesh, Svetha
Ashley, David
Phung, Dinh
Title of book Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I
Editor(s) Bailey, James
Khan, Latifur
Washio, Takashi
Dobbie, Gillian
Huang, Joshua Zhexue
Wang, Ruili
Publication date 2016
Series Lecture notes in artificial intelligence; v.9651
Chapter number 25
Total chapters 47
Start page 152
End page 164
Total pages 13
Publisher Springer
Place of Publication Berlin, Germany
Summary Treatments of cancer cause severe side effects called toxicities. Reduction of such effects is crucial in cancer care. To impact care, we need to predict toxicities at fortnightly intervals. This toxicity data differs from traditional time series data as toxicities can be caused by one treatment on a given day alone, and thus it is necessary to consider the effect of the singular data vector causing toxicity. We model the data before prediction points using the multiple instance learning, where each bag is composed of multiple instances associated with daily treatments and patient-specific attributes, such as chemotherapy, radiotherapy, age and cancer types. We then formulate a Bayesian multi-task framework to enhance toxicity prediction at each prediction point. The use of the prior allows factors to be shared across task predictors. Our proposed method simultaneously captures the heterogeneity of daily treatments and performs toxicity prediction at different prediction points. Our method was evaluated on a real-word dataset of more than 2000 cancer patients and had achieved a better prediction accuracy in terms of AUC than the state-of-art baselines.
ISBN 9783319317533
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-31753-3_9
Field of Research 080109 Pattern Recognition and Data Mining
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 ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083254

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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