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

Cheng Li, Sunil Gupta, Santu Rana, Wei Luo, Svetha Venkatesh, David Ashely, Dinh Phung

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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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part I
EditorsJames Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang
Place of PublicationCham Switzerland
Number of pages13
ISBN (Electronic)9783319317533
ISBN (Print)9783319317526
Publication statusPublished - 2016
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2016 - Auckland, New Zealand
Duration: 19 Apr 201622 Apr 2016
Conference number: 20th

Publication series

NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2016
Abbreviated titlePAKDD 2016
CountryNew Zealand
Internet address

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