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Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Julianna OlahWin Lee Edwin WongAtta-Ul Raheem Rana ChaudhryOmar MenaSunny X Tang
Published in: medRxiv : the preprint server for health sciences (2024)
Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.
Keyphrases
  • bipolar disorder
  • major depressive disorder
  • machine learning
  • deep learning
  • climate change
  • ms ms
  • healthcare
  • high throughput
  • working memory
  • high resolution
  • health information
  • clinical evaluation