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

Diagnostic Tools in Mental Health (242) Machine Learning (1465) Bipolar Disorders (56) Psychotic Disorders (161) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1515) Methods and New Tools in Mental Health Research (454) New Methods (945) Psychiatry (55)

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 Birnbaum1, 2, 3 Author Orcid Image ;   Avner Abrami4 Author Orcid Image ;   Stephen Heisig5 Author Orcid Image ;   Asra Ali1, 2 Author Orcid Image ;   Elizabeth Arenare1, 2 Author Orcid Image ;   Carla Agurto4 Author Orcid Image ;   Nathaniel Lu1, 2 Author Orcid Image ;   John M Kane1, 2, 3 Author Orcid Image ;   Guillermo Cecchi4 Author Orcid Image
Article Authors Cited by (15) 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 2023;259:121 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
  3. Yang H, He L, Li W, Zheng Q, Li Y, Zheng X, Zhang J. An automatic detection method for schizophrenia based on abnormal eye movements in reading tasks. Expert Systems with Applications 2024;238:121850 View
  4. Othmani A, Brahem B, Haddou Y, Mustaqeem . Machine-Learning-Based Approaches for Post-Traumatic Stress Disorder Diagnosis Using Video and EEG Sensors: A Review. IEEE Sensors Journal 2023;23(20):24135 View
  5. Loch A, Gondim J, Argolo F, Lopes-Rocha A, Andrade J, van de Bilt M, de Jesus L, Haddad N, Cecchi G, Mota N, Gattaz W, Corcoran C, Ara A. Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features. Schizophrenia Research 2023;258:45 View
  6. Zhang X, Fu X, Qi G, Zhang N. A multi‐scale feature fusion convolutional neural network for facial expression recognition. Expert Systems 2024;41(4) View
  7. Olah J, Spencer T, Cummins N, Diederen K. Automated analysis of speech as a marker of sub-clinical psychotic experiences. Frontiers in Psychiatry 2024;14 View
  8. Olah J, Cummins N, Arribas M, Gibbs-Dean T, Molina E, Sethi D, Kempton M, Morgan S, Spencer T, Diederen K. Towards a scalable approach to assess speech organization across the psychosis-spectrum -online assessment in conjunction with automated transcription and extraction of speech measures. Translational Psychiatry 2024;14(1) View
  9. Alsuhaibani M, Dodge H, Mahoor M. Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module. Expert Systems with Applications 2024;252:124185 View
  10. Perini I, Pabst A, Martinez D, Maurage P, Heilig M. Modeling social cognition in alcohol use disorder: lessons from schizophrenia. Psychopharmacology 2024 View
  11. Aina J, Akinniyi O, Rahman M, Odero-Marah V, Khalifa F. A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition. IEEE Access 2024;12:91410 View
  12. Kaczmarek‐Majer K, Dominiak M, Antosik A, Hryniewicz O, Kamińska O, Opara K, Owsiński J, Radziszewska W, Sochacka M, Święcicki Ł. Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: A prospective study. Acta Psychiatrica Scandinavica 2024 View