Explainable Zero-Shot Modelling of Clinical Depression Symptoms from Text

Farruque, Nawshad; Goebel, Randy; Zaı̈ane, Osmar R.; Sivapalan, Sudhakar

doi:10.1109/icmla52953.2021.00237

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…Foundation models have gained enormous popularity in the last few years with the rapid development of pre-training and self(semi)-supervised training methods [97], [98], especially since the release of OpenAI's ChatGPT. LLMs, along with visual [67], audio [78], and multimodal [99] foundation models were widely applied in many disciplines, including the mental health domain, but primarily limited to language analyses and self-rated (self-reported) conditions [100], [101]. By comparing the direct use of unimodal foundation modelgenerated embedding to manually defined features from the same modalities, we showed how they perform in more clinically-relevant tasks under the telehealth settings.…”

Section: B the Use Of Foundation Modelsmentioning

confidence: 99%

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns

Jiang,

Seyedi,

Griner

et al. 2023

Preprint

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ObjectiveThe current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders.MethodsTime series of multimodal features were derived from four conceptual modes: facial expression, vocal expression, linguistic expression, and cardiovascular modulation. The features were extracted from simultaneously recorded audio and video of remote interviews using task-specific and foundation models. Averages, standard deviations, and hidden Markov model-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination.ResultsStatistically significant feature differences were found between controls and subjects with mental health conditions. Correlations were found between features and self-rated depression and anxiety scores. Visual heart rate dynamics achieved the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities achieved AUROCs of 0.72-0.82. Features from task-specific models outperformed features from foundation models.ConclusionMultimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status.SignificanceThe proposed multimodal approach has the potential to facilitate objective, remote, and low-cost assessment for low-burden automated mental health services.

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Section: B the Use Of Foundation Modelsmentioning

confidence: 99%

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns

Jiang,

Seyedi,

Griner

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The left classifier uses Zero-shot modelling for classifying each sentence for signs of depression. Description of these classifiers including the datasets they were trained on, have been previously described (Farruque et al, 2019(Farruque et al, , 2021. Since we cannot extract more than 200 tokens for each of our posts and, within those 200 tokens, we may not have all the relevant tokens which are important for this task, we plan to extract relevant (or depressive) constituent sentences or excerpts from the posts which have more than 200 tokens and which are labeled as either carrying signs of "Moderate" or "Severe" depression in the training set.…”

Section: Extracting Relevant Excerpts For Fine-tuning Mental Bert (Re...mentioning

confidence: 99%

DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers

Farruque¹,

Zaı̈ane²,

Goebel³

et al. 2022

Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

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We discuss a variety of approaches for building a robust depression level detection model from longer social media posts (e.g., Reddit depression forum posts) using a mental health text informed pre-trained BERT model. Further, we report our experimental results based on a strategy to select excerpts from long text and then fine-tune the BERT model to combat the issue of memory constraints while processing such texts. We show that, with domain specific BERT, we can achieve reasonable accuracy with fixed text size (in this case 200 tokens). In addition we can use short text classifiers to extract relevant text from the long text and achieve some accuracy improvement, albeit, trading off with the processing time for extracting such excerpts.

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“…Recently, large language models have been used to automatically detect depression [19,32,67,72,74,75]. While these models are effective, they directly do not model user narratives.…”

Section: Depression Detection On Social Mediamentioning

confidence: 99%

DepressionNet: Learning Multi-modalities with User Post Summarization for Depression Detection on Social Media

Zogan

Razzak

Jameel

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publiclygenerated user data is also crucial to healthcare technologies because the discovered patterns would hugely benefit them in several ways. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns including user's social interactions. The downside is that these models are trained on several irrelevant content which might not be crucial towards detecting a depressed user. Besides, these content have a negative impact on the overall efficiency and effectiveness of the model. To overcome the shortcomings in the existing automatic depression detection methods, we propose a novel computational framework for automatic depression detection that initially selects relevant content through a hybrid extractive and abstractive summarization strategy on the sequence of all user tweets leading to a more fine-grained and relevant content. The content then goes to our novel deep learning framework comprising of a unified learning machinery comprising of Convolutional Neural Network (CNN) coupled with attention-enhanced Gated Recurrent Units (GRU) models leading to better empirical performance than existing strong baselines.

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Explainable Zero-Shot Modelling of Clinical Depression Symptoms from Text

Cited by 10 publications

References 8 publications

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns

DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers

DepressionNet: Learning Multi-modalities with User Post Summarization for Depression Detection on Social Media

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