Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-wise Missing Data.

IEEE Transactions on Biomedical Engineering(2019)

Cited 40|Views11
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Abstract
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 multi-source ensemble learning, a methodology which combines dataset deconstruction with ensemble learning ...
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Key words
Data models,Monitoring,Smart phones,Parkinson's disease,Biomarkers,Legged locomotion
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