Abstract
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 language | English |
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Title of host publication | Advances in Knowledge Discovery and Data Mining |
Subtitle of host publication | 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part I |
Editors | James Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 152-164 |
Number of pages | 13 |
ISBN (Electronic) | 9783319317533 |
ISBN (Print) | 9783319317526 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2016 - Auckland, New Zealand Duration: 19 Apr 2016 → 22 Apr 2016 Conference number: 20th http://pakdd16.wordpress.fos.auckland.ac.nz/ https://link.springer.com/book/10.1007/978-3-319-31753-3 (Proceedings) |
Publication series
Name | Lecture Notes in Artificial Intelligence |
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Publisher | Springer |
Volume | 9651 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2016 |
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Abbreviated title | PAKDD 2016 |
Country/Territory | New Zealand |
City | Auckland |
Period | 19/04/16 → 22/04/16 |
Internet address |