Results of the prediction models.

<div><p>The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalab...

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Bibliographic Details
Main Author:	Abir Elbéji (13988706) (author)
Other Authors:	Mégane Pizzimenti (20454603) (author), Gloria Aguayo (6039689) (author), Aurélie Fischer (6707654) (author), Hanin Ayadi (16842351) (author), Franck Mauvais-Jarvis (275208) (author), Jean-Pierre Riveline (781800) (author), Vladimir Despotovic (13988712) (author), Guy Fagherazzi (115092) (author)
Published:	2024
Subjects:	Biotechnology Sociology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified specified text recordings scalable screening method noninvasive screening tool innovative screening approaches american diabetes association 607 us participants stage t2d cases predict t2d status female t2d cases using hybrid byol ada risk score xlink "> study investigates specific algorithms pressing need performance improved overall agreement nct04848623 ). key factors globally calls cvt embeddings constructed gender best models based algorithm analyzed pre altman analysis 74 %)
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Results of the prediction models.

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