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Crowdsourced feature tagging for scalable and privacy-preserved autism diagnosis
[article]
2020
medRxiv
pre-print
Standard medical diagnosis of mental health conditions often requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently label features needed for accurate machine learning detection of the common childhood developmental disorder autism. We implement a novel process for creating a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from
doi:10.1101/2020.12.15.20248283
fatcat:rol2g6tf35fmhmkizrfe5xfjxy