Jennifer Schaff
Research Subjects
Research Interests
Jennifer Schaff has a total of 42 co-authors. Here are the top co-authors by number of shared publications:
- Aipeng Chen
Prince of Wales Clinical School
1 shared publication - Bálint Ármin Pataki
Department of Physics of Complex System, ELTE Eötvös Loránd University, Budapest, Hungary
1 shared publication - Carlos Espino
Early Signal
1 shared publication - Christian McDaniel
University of Georgia
1 shared publication - Daniela Brunner
Early Signal
1 shared publication - Dimitri Perrin
Queensland University of Technology
1 shared publication
Latest Publications
- Open Access
- 16 January 2020
- Published by Cold Spring Harbor Laboratory in bioRxiv
Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. 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 Parkinson’s Disease (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 (best AUPR=0.95) severity.
- 16 January 2020
- Published by Cold Spring Harbor Laboratory in bioRxiv
Open AccessCrowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. 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 Parkinson’s Disease (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 (best AUPR=0.95) severity.