Browsing by Author "Wang, Yuanjia"

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    Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
    (Springer Nature, 2021-03-19) Sieberts, Solveig K.; Schaff, Jennifer; Duda, Marlena; Pataki, Bálint Ármin; Sun, Ming; Snyder, Phil; Daneault, Jean-Francois; Parisi, Federico; Costante, Gianluca; Rubin, Udi; Banda, Peter; Chae, Yooree; Neto, Elias Chaibub; Dorsey, E. Ray; Aydın, 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; Parkinson’s Disease Digital Biomarker Challenge Consortium; Wang, Yuanjia; Guan, Yuanfang; Brunner, Daniela; Bonato, Paolo; Mangravite, Lara M.; Omberg, Larsson; Medicine, School of Medicine
    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).
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    Tools for communicating risk for Parkinson's disease
    (Springer Nature, 2022-11-29) Cook, Lola; Schulze, Jeanine; Uhlmann, Wendy R.; Verbrugge, Jennifer; Marder, Karen; Lee, Annie J.; Wang, Yuanjia; Alcalay, Roy N.; Nance, Martha; Beck, James C.; Medical and Molecular Genetics, School of Medicine
    We have greater knowledge about the genetic contributions to Parkinson’s disease (PD) with major gene discoveries occurring in the last few decades and the identification of risk alleles revealed by genome-wide association studies (GWAS). This has led to increased genetic testing fueled by both patient and consumer interest and emerging clinical trials targeting genetic forms of the disease. Attention has turned to prodromal forms of neurodegenerative diseases, including PD, resulting in assessments of individuals at risk, with genetic testing often included in the evaluation. These trends suggest that neurologists, clinical geneticists, genetic counselors, and other clinicians across primary care and various specialties should be prepared to answer questions about PD genetic risks and test results. The aim of this article is to provide genetic information for professionals to use in their communication to patients and families who have experienced PD. This includes up-to-date information on PD genes, variants, inheritance patterns, and chances of disease to be used for risk counseling, as well as insurance considerations and ethical issues.