Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 8; pp. 3357 - 3371
• Main Authors: Huang, Zhongwei, Lei, Haijun, Chen, Guoliang, Frangi, Alejandro F., Xu, Yanwu, Elazab, Ahmed, Qin, Jing, Lei, Baiying
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Algorithms; Biomedical imaging; Classification; clinical score prediction; Data structures; Diseases; Embedding; embedding learning; Feature extraction; Iterative methods; longitudinal multimodal data; Lung; Machine learning; Manifolds (mathematics); Movement disorders; Neurodegenerative diseases; Optimization; Parkinson's disease; Parkinson’s disease (PD); Regression; Similarity; Sparse matrices; sparse regression; Task analysis; Ultrasonic imaging

Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised featu...