Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients’ daily disease and
symptom burden, particularly in the case of movement disorders like Parkinson’s Disease (PD).
However, interpreting these complex data into so-called digital biomarkers requires complicated
analytical approaches, and validating these biomarkers requires sufficient data and unbiased
evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and
benchmark features derived from accelerometer and gyroscope data in two different datasets to
predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia and
bradykinesia. Forty teams from around the world submitted features, and achieved drastically
improved predictive performance for PD status (best AUROC=0.87), as well as tremor- (best
AUPR=0.75), dyskinesia- (best AUPR=0.48) and bradykinesia-severity (best AUPR=0.95).