作者
Yuji Zhao, Max A Laansma, Eva M van Heese, Conor Owens-Walton, Laura M Parkes, Ines Debove, Christian Rummel, Roland Wiest, Fernando Cendes, Rachel Guimaraes, Clarissa Lin Yasuda, Jiun-Jie Wang, Tim J Anderson, John C Dalrymple-Alford, Tracy R Melzer, Toni L Pitcher, Reinhold Schmidt, Petra Schwingenschuh, Gäetan Garraux, Mario Rango, Letizia Squarcina, Sarah Al-Bachari, Hedley CA Emsley, Johannes C Klein, Clare E Mackay, Michiel F Dirkx, Rick Helmich, Francesca Assogna, Fabrizio Piras, Joanna K Bright, Gianfranco Spalletta, Kathleen Poston, Christine Lochner, Corey T McMillan, Daniel Weintraub, Jason Druzgal, Benjamin Newman, Odile A Van Den Heuvel, Neda Jahanshad, Paul M Thompson, Ysbrand D van der Werf, Boris Gutman, ENIGMA consortium
发表日期
2022/9/18
图书
International Workshop on Machine Learning in Clinical Neuroimaging
页码范围
115-124
出版商
Springer Nature Switzerland
简介
We extend the sparse, spatially piecewise-contiguous linear classification framework for mesh-based data to ordinal logistic regression. The algorithm is intended for use with subcortical shape and cortical thickness data where progressive clinical staging is available, as is generally the case in neurodegenerative diseases. We apply the tool to Parkinson’s and Alzheimer’s disease staging. The resulting biomarkers predict Hoehn-Yahr and cognitive impairment stages at competitive accuracy; the models remain parsimonious and outperform one-against-all models in terms of the Akaike and Bayesian information criteria.
学术搜索中的文章
Y Zhao, MA Laansma, EM van Heese… - International Workshop on Machine Learning in …, 2022