A Ranking-based Weakly Supervised Learning model for telemonitoring of Parkinson’s disease
Author:
Affiliation:
1. School of Industrial and Systems Engineering, Georgia Tech, Atlanta, GA, USA
2. Department of Neurology, Emory University, Atlanta, GA, USA
Funder
NSF
Publisher
Informa UK Limited
Subject
Public Health, Environmental and Occupational Health,Safety Research,Safety, Risk, Reliability and Quality
Link
https://www.tandfonline.com/doi/pdf/10.1080/24725579.2022.2091065
Reference62 articles.
1. Abujrida, H., Agu, E. & Pahlavan, K. (2017, November). Smartphone-based gait assessment to infer Parkinson's disease severity using crowdsourced data. 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT) (pp. 208–211). IEEE.
2. Arora, S., Venkataraman, V., Donohue, S., Biglan, K. M., Dorsey, E. R. & Little, M. A. (2014, May). High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3641–3644). IEEE.
3. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study
4. Blum, A. & Chawla, S. (2001). Learning from labeled and unlabeled data using graph mincuts. Proceedings of the Eighteenth International Conference on Machine Learning (pp. 19–26).
5. The mPower study, Parkinson disease mobile data collected using ResearchKit
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