Multimodal Detection of Depression in Clinical Interviews

@article{Dibekliolu2015MultimodalDO,
  title={Multimodal Detection of Depression in Clinical Interviews},
  author={Hamdi Dibeklioğlu and Zakia Hammal and Ying Yang and Jeffrey F. Cohn},
  journal={Proceedings of the 2015 ACM on International Conference on Multimodal Interaction},
  year={2015},
  url={https://api.semanticscholar.org/CorpusID:2810891}
}
It is suggested that automatic detection of depression from behavioral indicators is feasible and that multimodal measures afford most powerful detection.
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79 Citations
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Background Citations
28
Methods Citations
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79 Citations

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29 References

Detecting depression from facial actions and vocal prosody

The findings suggest the feasibility of automatic detection of depression, raise new issues in automated facial image analysis and machine learning, and have exciting implications for clinical theory and practice.

Detecting Depression Severity from Vocal Prosody

It is suggested that analysis of vocal prosody could be a powerful tool to assist in depression screening and monitoring over the course of depressive disorder and recovery.

Eye movement analysis for depression detection

This paper analyses the performance of eye movement features extracted from face videos using Active Appearance Models for a binary classification task (depressed vs. non-depressed) and finds that eye movement low-level features gave 70% accuracy using a hybrid classifier of Gaussian Mixture Models and Support Vector Machines and 75% accuracy when using statistical measures with SVM classifiers over the entire interview.

Can body expressions contribute to automatic depression analysis?

The results show the effectiveness of the proposed system to evaluate the contribution of various body parts in depression analysis, based on a framework based on space-time interest points and bag of words.

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A review of depression and suicide risk assessment using speech analysis

A RATING SCALE FOR DEPRESSION

The present scale has been devised for use only on patients already diagnosed as suffering from affective disorder of depressive type, used for quantifying the results of an interview, and its value depends entirely on the skill of the interviewer in eliciting the necessary information.

Antidepressant drug effects and depression severity: a patient-level meta-analysis.

The magnitude of benefit of antidepressant medication compared with placebo increases with severity of depression symptoms and may be minimal or nonexistent, on average, in patients with mild or moderate symptoms.