Cargando…
Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition
Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers—defined as vetted members of popular crowdsourcing platforms—to aid in the task of diagnosing aut...
Autores principales: | Washington, Peter, Leblanc, Emilie, Dunlap, Kaitlyn, Penev, Yordan, Kline, Aaron, Paskov, Kelley, Sun, Min Woo, Chrisman, Brianna, Stockham, Nathaniel, Varma, Maya, Voss, Catalin, Haber, Nick, Wall, Dennis P. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7564950/ https://www.ncbi.nlm.nih.gov/pubmed/32823538 http://dx.doi.org/10.3390/jpm10030086 |
Ejemplares similares
-
Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection
por: Washington, Peter, et al.
Publicado: (2021) -
Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder
por: Washington, Peter, et al.
Publicado: (2021) -
Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks
por: Washington, Peter, et al.
Publicado: (2019) -
Addendum to the Acknowledgements: Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks
por: Washington, Peter, et al.
Publicado: (2019) -
Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections
por: Washington, Peter, et al.
Publicado: (2022)