Cargando…
Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) using smartphones. This contribution presents mu...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
IEEE
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487914/ https://www.ncbi.nlm.nih.gov/pubmed/30403615 http://dx.doi.org/10.1109/TBME.2018.2873252 |
Ejemplares similares
-
Super ensemble based streamflow simulation using multi-source remote sensing and ground gauged rainfall data fusion
por: Wegayehu, Eyob Betru, et al.
Publicado: (2023) -
Multi-Source Transfer Learning via Ensemble Approach for Initial Diagnosis of Alzheimer’s Disease
Publicado: (2020) -
Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case
por: Huang, Jianping, et al.
Publicado: (2023) -
Predicting residential structures from open source remotely enumerated data using machine learning
por: Sturrock, Hugh J. W., et al.
Publicado: (2018) -
Multi-Source Remotely Sensed Data Combination: Projection Transformation Gap-Fill Procedure
por: Boloorani, Ali Darvishi, et al.
Publicado: (2008)