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• W3111231448 abstract "ABSTRACT 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 a pool of 1,107. Two previously validated binary autism logistic regression classifiers were used to evaluate the quality of the curated crowd’s ratings on unstructured home videos. A clinically representative balanced sample (N=50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores >0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels that exceed classification methods without alterations. We find that machine learning classification from features extracted by a curated nonexpert crowd achieves clinical performance for pediatric autism videos and maintains acceptable performance when privacy-preserving mechanisms are applied. These results suggest that privacy-based crowdsourcing of short videos can be leveraged for rapid and mobile assessment of behavioral health." @default.
• W3111231448 created "2020-12-21" @default.
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• W3111231448 date "2020-12-17" @default.
• W3111231448 modified "2023-10-16" @default.
• W3111231448 title "Crowdsourced feature tagging for scalable and privacy-preserved autism diagnosis" @default.
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• W3111231448 doi "https://doi.org/10.1101/2020.12.15.20248283" @default.
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