Parkinson's disease is a neurodegenerative disease that can affect a person's
movement, speech, dexterity, and cognition. Clinicians primarily diagnose
Parkinson's disease by performing a clinical assessment of symptoms. However,
misdiagnoses are common. One factor that contributes to misdiagnoses is that
the symptoms of Parkinson's disease may not be prominent at the time the
clinical assessment is performed. Here, we present a machine-learning approach
towards distinguishing between people with and without Parkinson's disease
using long-term data from smartphone-based walking, voice, tapping and memory
tests. We demonstrate that our attentive deep-learning models achieve
significant improvements in predictive performance over strong baselines (area
under the receiver operating characteristic curve = 0.85) in data from a cohort
of 1853 participants. We also show that our models identify meaningful features
in the input data. Our results confirm that smartphone data collected over
extended periods of time could in the future potentially be used as a digital
biomarker for the diagnosis of Parkinson's disease.

使用智能手机的长期数据来诊断帕金森病，研究表明，深度学习模型的表现显著优于传统方法，smartphone 数据可以作为未来可能用于帕金森病诊断的数字生物标志物。