Using deeply time-series semantics to assess depressive symptoms based on clinical interview speech.

Li, Nanxi; Feng, Lei; Hu, Jiaxue; Jiang, Lei; Wang, Jing; Han, Jiali; Gan, Lu; He, Zhiyang; Wang, Gang

Li, Nanxi; Feng, Lei; Hu, Jiaxue; Jiang, Lei; Wang, Jing; Han, Jiali; Gan, Lu; He, Zhiyang; Wang, Gang.

Afiliación

Li N; Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Feng L; Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Hu J; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Jiang L; Anhui iFLYTEK Health Co., Ltd., Hefei, China.
Wang J; Anhui iFLYTEK Health Co., Ltd., Hefei, China.
Han J; Anhui iFLYTEK Health Co., Ltd., Hefei, China.
Gan L; Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
He Z; Anhui iFLYTEK Health Co., Ltd., Hefei, China.
Wang G; Anhui iFLYTEK Health Co., Ltd., Hefei, China.

Front Psychiatry ; 14: 1104190, 2023.

Article en En | MEDLINE | ID: mdl-36865077

Palabras clave

depression; mood disorder; natural language processing; psychiatric assessment; semantic; time-series

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Qualitative_research Idioma: En Revista: Front Psychiatry Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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