Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Physicians 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 healthy people and people with Parkinson's disease using long-term data collected from smartphone-based tests, including walking, voice, tapping and memory tests. We demonstrate that the presented approach leads to significant performance improvements over existing methods (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants.  Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as additional evidence for the diagnosis of Parkinson's disease.
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.
R
Physicians
Clinicians
R
healthy people and people with
people with and without
D
collected
D
tests, including
R
the presented approach leads to significant performance improvements over existing methods
our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines
A
We also show that our models identify meaningful features in the input data.
R
additional evidence
a digital biomarker