Detecting Clinically Relevant Emotional Distress and Functional Impairment in Children and Adolescents: Protocol for an Automated Speech Analysis Algorithm Development Study.

Alemu, Yared; Chen, Hua; Duan, Chenghao; Caulley, Desmond; Arriaga, Rosa I; Sezgin, Emre

Alemu, Yared; Chen, Hua; Duan, Chenghao; Caulley, Desmond; Arriaga, Rosa I; Sezgin, Emre.

Afiliación

Alemu Y; TQIntelligence, Inc, Tucker, GA, United States.
Chen H; Department of Psychiatry and Behavioral Sciences, Morehouse School of Medicine, Atlanta, GA, United States.
Duan C; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Caulley D; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Arriaga RI; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Sezgin E; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.

JMIR Res Protoc ; 12: e46970, 2023 Jun 23.

Article en En | MEDLINE | ID: mdl-37351936

Palabras clave

adverse childhood experiences; artificial intelligence; at-risk youth; machine learning; mental health; pediatrics; predictive modeling; social determinants of health; speech biomarker; speech-recognition; trauma and emotional distress; voice marker

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Aspecto: Determinantes_sociais_saude / Equity_inequality Idioma: En Revista: JMIR Res Protoc Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá

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