Maya Varma

TL;DR: Digital phenotyping of autism is paving the way for quantitative psychiatry more broadly and will set the stage for more scalable, accessible, and precise diagnostic techniques in the field.

TL;DR: The findings show that a single CNN model can be effectively utilized for the identification of diverse abnormalities in highly variable radiographs of multiple body parts, a result that holds potential for improving patient triage and assisting with diagnostics in resource-limited settings.

TL;DR: This work designs a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data) and presents Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices.

TL;DR: This study evaluates 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 autism and proposes a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions.

TL;DR: Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20% from professional gold standard raters, which is potentially a clinically informative estimate for parents.