Detection and Prediction of Clinical Depression

Lech, Margaret; Low, Lu-Shih Alex; Ooi, Kuan Ee Brian

doi:10.1007/978-3-642-38550-6_10

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Significant related work has been done on the automatic analysis and prediction of depression using speech and prosody [28,21,23,46], gestures, head pose, and facial expressions [2,33,43,19], and a multimodal combination of these cues [49,20,27,26]. As described in the literature, there are a variety of challenges in automatically determining whether a person is depressed, including: 1) each behavioral signal provides only partial information, which must be combined to form a more realistic model for recognizing behaviors indicative of depression; 2) some information that is relevant may not be available or may be inherently hidden; 3) defining baseline behavior can be difficult with limited behavioral data.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions

Gupta

Malandrakis

Xiao

et al. 2014

Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge

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Depression is one of the most common mood disorders. Technology has the potential to assist in screening and treating people with depression by robustly modeling and tracking the complex behavioral cues associated with the disorder (e.g., speech, language, facial expressions, head movement, body language). Similarly, robust affect recognition is another challenge which stands to benefit from modeling such cues. The Audio/Visual Emotion Challenge (AVEC) aims toward understanding the two phenomena and modeling their correlation with observable cues across several modalities. In this paper, we use multimodal signal processing methodologies to address the two problems using data from human-computer interactions. We develop separate systems for predicting depression levels and affective dimensions, experimenting with several methods for combining the multimodal information. The proposed depression prediction system uses a feature selection approach based on audio, visual, and linguistic cues to predict depression scores for each session. Similarly, we use multiple systems trained on audio and visual cues to predict the affective dimensions in continuous-time. Our affect recognition system accounts for context during the frame-wise inference and performs a linear fusion of outcomes from the audio-visual systems. For both problems, our proposed systems outperform the videofeature based baseline systems. As part of this work, we analyze the role played by each modality in predicting the target variable and provide analytical insights.

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Section: Introductionmentioning

confidence: 99%

Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions

Gupta

Malandrakis

Xiao

et al. 2014

Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge

View full text Add to dashboard Cite

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“…The Institute for Health Metrics and Evaluation depicts the progression of depression over the years as stable disease, as also confirmed by [Brody et al 2018]. [Lech et al 2014] highlights the urgency in early identification and prediction of depression and its symptoms due to more positive results if treated soon. Sometimes, it is costly to get professional help due to the location or economic scenario.…”

Section: Introductionmentioning

confidence: 67%

Study about Gathering Features in Depression Detection’ Problem with Health Professionals Community

Filho

Silva

Oliveira

et al. 2022

iSys

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Understanding individuals, social dynamics, and data consumption within social media platforms arouse curiosity and attention in the scientific community and society. The scientific community has shown how a user's mental health can be affected by technology and its digital environment. For example, a user exposed to constant explicit hate speech may suffer an impact on its well-being. There are already efforts in this research area that propose automated solutions to identify users who require professional health attention. However, these solutions do not frequently use the experience and background from the health acknowledgment area in their contribution construction. To fill this gap, we propose a qualitative feature validation with two stages to identify which characteristics are relevant to health professionals, aiming at machine learning and deep learning solutions to depression detection. First, we validate this set of features using a semi-structured interview with three psychologists. Afterward, we apply a survey with domain experts to validate the information extracted from the first stage. This feature validation will allow us to have a detailed view of how functional and practical are the features commonly used in machine-learning-based solutions and how they are close to clinical analysis.

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Detection and Prediction of Clinical Depression

Cited by 2 publications

References 23 publications

Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions

Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions

Study about Gathering Features in Depression Detection’ Problem with Health Professionals Community

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