A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge
Tekijät: Sieberts SolveigK, Schaff Jennifer, Duda Marlena, Pataki Bálint Ármin, Sun Ming, Snyder Phil, Daneault Jean-Francois, Parisi Federico, Costante Gianluca, Rubin Udi, Banda Peter, Chae Yoree, Neto Elias Chaibub, Dorsey E Ray, Aydin Zafer, Chen Aipeng, Elo Laura L, Espino Carlos, Glaab Enrico, Goan Ethan, Golabchi Fatemeh Noushin, Görmez Yasin, Jaakkola Maria K, Jonnagaddala Jitendra, Klén Riku, Li Dongmei, McDaniel Christian, Perrin Dimitri, Perumal Thanneer M, Rad Nastaran Mohammadian, Rainaldi Erin, Sapienza Stefano, Schwab Patrick, Shokhirev Nikolai, Venäläinen Mikko S, Vergara-Diaz Gloria, Zhang Yuqian, Wang Yuanjian, Guan Yuanfang, Brunner Daniela, Bonato Paolo, Mangravite Lara M, Omberg Larsson; the Parkinson's Disease Digital Biomarker Challenge Consortium
Kustantaja: NATURE RESEARCH
Julkaisuvuosi: 2021
Journal: npj Digital Medicine
Tietokannassa oleva lehden nimi: NPJ DIGITAL MEDICINE
Lehden akronyymi: NPJ DIGIT MED
Artikkelin numero: ARTN 53
Vuosikerta: 4
Sivujen määrä: 12
ISSN: 2398-6352
eISSN: 2398-6352
DOI: https://doi.org/10.1038/s41746-021-00414-7
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/54773626
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).
Ladattava julkaisu This is an electronic reprint of the original article. |  |
