Robust Speech and Natural Language Processing Models for Depression Screening

Lü, Yang; Harati, Amir; Rutowski, Tomasz; Oliveira, Ricardo Augusto Rabelo; Chlebek, Piotr; Shriberg, Elizabeth

doi:10.1109/spmb50085.2020.9353611

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Along similar lines, many studies that analyze audio and visual data of patients with SCZ have demonstrated abnormalities in language [14][15][16] , speech [17][18][19][20][21] , facial expressions [21][22][23] , and motor [24][25][26] behaviors. Similar studies of patients with MDD have identified abnormalities in verbal [27][28][29][30] and nonverbal behaviors [31][32][33][34][35][36] , facial expressions 34,37,38 , and body movement [39][40][41] associated with MDD. This stream of the literature suggests that digital phenotyping is a promising avenue toward objective behavioral measures for characterizing mental disorders.…”

Section: Introductionmentioning

confidence: 64%

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Yang

Chakraborty

et al. 2022

Schizophr

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Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

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Section: Introductionmentioning

confidence: 64%

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Yang

Chakraborty

et al. 2022

Schizophr

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“… 169 gait-only video – casual walking in a corridor diagnosis LSTM + CNN weighted fusion 40 videos Lu et al. 170 language – answers to personal questions diagnosis LSTM fine-tuned with health forum data 2,425 subjects Eichstaedt et al. 171 language – Facebook posts risk assessment logistic regression 68 patients Sun et al.…”

Section: Ml-powered Technologies For Psychiatrymentioning

confidence: 99%

“… 194 On the other hand, data collected from free-speech samples for diagnostic purposes can be highly effective for developing a language-based depression screening that generalizes well across various age groups. 170 , 195 , 198 …”

Section: Ml-powered Technologies For Psychiatrymentioning

confidence: 99%

Modern views of machine learning for precision psychiatry

Chen¹,

Kulkarni²,

Galatzer‐Levy³

et al. 2022

Patterns

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“…For example, for predicting first episode psychosis, language data from clinical tests has higher performance compared to transcripts of free speech [205]. On the other hand, data collected "free-speech" samples for diagnostic purposes has been found to be highly effective in developing a language-based depression screening that generalizes well across various age groups [167], [184], [206].…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

Modern Views of Machine Learning for Precision Psychiatry

Chen¹,

Prathamesh²,

Kulkarni³

et al. 2022

Preprint

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In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

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Robust Speech and Natural Language Processing Models for Depression Screening

Cited by 6 publications

References 18 publications

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Modern views of machine learning for precision psychiatry

Modern Views of Machine Learning for Precision Psychiatry

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