Abstract
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.
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Acknowledgments
Work by BG and YZ was supported by the Alzheimer’s Association grant 2018-AARG-592081, Advanced Disconnectome Markers of Alzheimer’s Disease. ENIGMA-PD (YW, PT, EH, ML) is supported by NINDS award 1RO1NS107513-01A.
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Zhao, Y. et al. (2022). Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2022. Lecture Notes in Computer Science, vol 13596. Springer, Cham. https://doi.org/10.1007/978-3-031-17899-3_12
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DOI: https://doi.org/10.1007/978-3-031-17899-3_12
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