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Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-task Framework

Version 2 2024-06-02, 00:13
Version 1 2016-05-05, 13:01
conference contribution
posted on 2024-06-02, 00:13 authored by Cheng Li, Sunil GuptaSunil Gupta, Santu RanaSantu Rana, Wei LuoWei Luo, Svetha VenkateshSvetha Venkatesh, David Ashely, Dinh Phung
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.

History

Volume

9651

Chapter number

25

Pagination

152-164

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319317526

Language

eng

Publication classification

B Book chapter, B1 Book chapter

Copyright notice

2016, Springer

Extent

47

Editor/Contributor(s)

Bailey J, Khan L, Washio T, Dobbie G, Huang JZ, Wang R

Publisher

Springer International Publishing

Place of publication

Berlin, Germany

Title of book

Advances in Knowledge Discovery and Data Mining

Series

Lecture notes in artificial intelligence; v.9651

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