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2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 778-785
Michael T. Lash , Department of Computer Science, University of Iowa, Iowa City, Iowa 52242
Yuqi Sun , Department of Computer Science, University of Iowa, Iowa City, Iowa 52242
Xun Zhou , Department of Management Sciences, University of Iowa, Iowa City, Iowa 52242
Charles F. Lynch , Department of Epidemiology, University of Iowa, Iowa City, Iowa 52242
W. Nick Street , Department of Management Sciences, University of Iowa, Iowa City, Iowa 52242
ABSTRACT
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model performance using a newly defined metric – area between the curves (ABC) – to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark. We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.
INDEX TERMS
Cancer, Indexes, Predictive models, Neural networks, Spectral analysis, Geography, Smoothing methods
CITATION

M. T. Lash, Y. Sun, X. Zhou, C. F. Lynch and W. N. Street, "Learning rich geographical representations: Predicting colorectal cancer survival in the state of Iowa," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 778-785.
doi:10.1109/BIBM.2017.8217754
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