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Toxicity prediction in cancer using multiple instance learning in a multi-task framework
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posted on 2016-01-01, 00:00 authored by Cheng Li, Sunil GuptaSunil Gupta, Santu RanaSantu Rana, Wei LuoWei Luo, Svetha VenkateshSvetha Venkatesh, David Ashley, Quoc-Dinh PhungTreatments 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.
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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 IVolume
9651Series
Lecture notes in artificial intelligence; v.9651Chapter number
25Pagination
152 - 164Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743ISBN-13
9783319317533Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2016, SpringerExtent
47Editor/Contributor(s)
J Bailey, L Khan, T Washio, G Dobbie, J Huang, R WangUsage metrics
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