Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson’s disease
oleh: Kaiwen Deng, Yueming Li, Hanrui Zhang, Jian Wang, Roger L. Albin, Yuanfang Guan
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2022-01-01 |
Deskripsi
Deng et al. develop deep learning methods that identify Parkinson’s Disease (PD) patients using public accelerometer and position data with higher accuracy than when using gait/rest and voice-based models. Their study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.