Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

ENIGMA consortium (2022) Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In: Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer Science and Business Media Deutschland GmbH, SGP, pp. 115-124. ISBN 9783031178986

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.