Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Published in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017
Recommended 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, in Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference on, pp. 778-785, 2017. http://michael-lash.github.io/files/rich_geo_reps_bibm_2017.pdf
In this work, we propose a method to learn latent representations from spatial (georaphic) data to predict colorectal cancer survival curves, specifically focusing on data covering the US state of Iowa.
Recommended 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, in Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference on, pp. 778-785, 2017.