This paper is in the following e-collection/theme issue:

Diagnostic Tools in Mental Health (191) Machine Learning (1121) Bipolar Disorders (52) Psychotic Disorders (138) e-Mental Health and Cyberpsychology (1122) Methods and New Tools in Mental Health Research (383) New Methods (750) Psychiatry (26)

Published on in Vol 9 , No 1 (2022) :January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24699, first published .
Acoustic and Facial Features From Clinical Interviews for Machine Learning–Based Psychiatric Diagnosis: Algorithm Development

Acoustic and Facial Features From Clinical Interviews for Machine Learning–Based Psychiatric Diagnosis: Algorithm Development

Acoustic and Facial Features From Clinical Interviews for Machine Learning–Based Psychiatric Diagnosis: Algorithm Development

Authors of this article:

Michael L Birnbaum 1, 2, 3 Author Orcid Image ;   Avner Abrami 4 Author Orcid Image ;   Stephen Heisig 5 Author Orcid Image ;   Asra Ali 1, 2 Author Orcid Image ;   Elizabeth Arenare 1, 2 Author Orcid Image ;   Carla Agurto 4 Author Orcid Image ;   Nathaniel Lu 1, 2 Author Orcid Image ;   John M Kane 1, 2, 3 Author Orcid Image ;   Guillermo Cecchi 4 Author Orcid Image
Article Authors Cited by (2) Tweetations (12) Metrics

Journals

  1. Granrud O, Rodriguez Z, Cowan T, Masucci M, Cohen A. Alogia and pressured speech do not fall on a continuum of speech production using objective speech technologies. Schizophrenia Research 2022 View
  2. Chen Z, Kulkarni P, Galatzer-Levy I, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3(11):100602 View