Ying-Cong Chen
Ying-Cong Chen has 2 papers in the ChatPPG research atlas (2021–2022) and 15 co-authors in the atlas. Main topics: parkinsons, breathing, rf-sensing, physiological-monitoring, imbalanced-regression, methodology.
Papers
- Artificial Intelligence-Enabled Detection and Assessment of Parkinson's Disease using Nocturnal Breathing Signals2022 · Nature Medicine 2022
This Nature Medicine study built an AI model that detects Parkinson's disease and tracks its progression from breathing signals recorded during sleep. Evaluated on 7,671 people from several US hospitals and public datasets, it detected the disease with AUC 0.90 on held-out data and 0.85 on external data, and its severity estimates matched the MDS-UPDRS rating scale (R = 0.94). Breathing can be captured without contact at home from radio waves reflected off the body.
- Published: Nature Medicine, 2022
- Cohort: 7,671 individuals
- Signals: nocturnal breathing (incl. radio-wave sensing)
- Delving into Deep Imbalanced Regression2021 · arXiv preprint
This paper addresses regression problems where some target values have far fewer examples than others. The authors propose smoothing both the label and feature distributions so that nearby target values inform each other, which calibrates learned features for continuous labels.
- Published: arXiv preprint, 2021
- Problem: imbalanced regression (continuous targets)
- Method: label distribution smoothing + feature distribution smoothing