Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson’s Disease
Abstract In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predicti...
Guardado en:
Autores principales: | Chao Gao, Hanbo Sun, Tuo Wang, Ming Tang, Nicolaas I. Bohnen, Martijn L. T. M. Müller, Talia Herman, Nir Giladi, Alexandr Kalinin, Cathie Spino, William Dauer, Jeffrey M. Hausdorff, Ivo D. Dinov |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f3f7a8e86fd34267bf9c85a913def857 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Predictive models for the diagnostic of human visceral leishmaniasis in Brazil.
por: Tália S Machado de Assis, et al.
Publicado: (2012) -
Protocol for the DeFOG trial: A randomized controlled trial on the effects of smartphone-based, on-demand cueing for freezing of gait in Parkinson's disease
por: Demi Zoetewei, et al.
Publicado: (2021) -
Test-level and Item-level Model Fit Comparison of General vs. Specific Diagnostic Classification Models: A Case of True DCM
por: Mahdieh Shafipoor, et al.
Publicado: (2021) -
Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson’s disease using electrophysiological measurements of visual processing
por: Freja Gam Østergaard, et al.
Publicado: (2020) -
Robust diagnostic classification via Q-learning
por: Victor Ardulov, et al.
Publicado: (2021)