Article ; Online: Crowdsourcing digital health measures to predict Parkinson’s disease severity
npj Digital Medicine, Vol 4, Iss 1, Pp 1-
the Parkinson’s Disease Digital Biomarker DREAM Challenge
2021 Volume 12
Abstract: Abstract 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 ... ...
| Abstract | Abstract 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). |
|---|---|
| Keywords | Computer applications to medicine. Medical informatics ; R858-859.7 |
| Subject code | 006 |
| Language | English |
| Publishing date | 2021-03-01T00:00:00Z |
| Publisher | Nature Portfolio |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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