Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety | Zendy

George Demiris | Zendy; Kristin L Corey Magan | Zendy; Debra Parker Oliver | Zendy; Karla Washington | Zendy; Chad Chadwick | Zendy; Jeffrey Voigt | Zendy; Sam Brotherton | Zendy; Mary D. Naylor | Zendy

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Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety

Author(s) -

George Demiris,

Kristin L Corey Magan,

Debra Parker Oliver,

Karla Washington,

Chad Chadwick,

Jeffrey Voigt,

Sam Brotherton,

Mary D. Naylor

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa049

Subject(s) - anxiety , interview , classifier (uml) , machine learning , computer science , artificial intelligence , recall , set (abstract data type) , clinical psychology , psychology , cognitive psychology , psychiatry , political science , law , programming language

The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety.

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