A Machine Learning Implementation for Mental Health Care. Application: Smart Watch for Depression Detection

Depression is a serious mental health condition that may lead to poor mental and emotional functioning at work, at school and in the family causing the mental imbalance. In worst scenarios, depression may lead to severe anxiety or suicide. Hence, it is necessary to diagnose depression at early stages. This paper elaborates the development of a novel approach for a convolutional neural network model that can examine facial images from the recorded interview sessions to discover facial patterns that could indicate depression level. The user‐generated data helps to distinguish between different depressive groups with depression symptoms that can manifest people with various mental illnesses in different ways. In particular, we want to automatically predict the depression scale and differentiate depression from other mental disorders using the patient's psychiatric illness history and dynamic textual descriptions extracted from the user inputs. We apply the k‐nearest neighbour algorithm on the dynamic textual descriptors to make a linguistic analysis for classifying mental illness into different classes. We apply dimensionality reduction and regression using the Random Forest algorithm to predict the depression scale. The proposed framework is an extension to pre‐existing frameworks, replacing the handcrafted feature extraction technique with the deep feature extraction. The model performs 2.7% better than existing frameworks in facial detection and feature extraction.

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“…The data for features extracted might be too huge to directly represent as a vector for representing faces. (Kumar et al, 2021).…”

Section: Visual Feature Extractionmentioning

confidence: 99%

RETRACTED: Diagnosis of depression level using multimodal approaches using deep learning techniques with multiple selective features

Meshram

Rambola

2022

Expert Systems

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“…In addition, there are also many problems such as social maladjustment, interpersonal tension, and economic pressure. Therefore, in the face of these new psychological characteristics and psychological phenomena, it is necessary to make new improvements in the direction of mental health education in colleges and universities; finally, the methods of mental health education in colleges and universities also need to be further improved in today's highly developed information technology (Kumar et al, 2021). Educational methods more scientific and technological forces should be infiltrated.…”

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confidence: 99%

RETRACTED: Model innovation of students' mental health education from the perspective of big data

Bin

2022

Expert Systems

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To improve the effect of college students' mental health education, and to meet the needs of mental health education data processing, this article combines big data technology and gene regulation ideas to propose a mental health education data processing algorithm based on big data technology and immunotherapy gene regulation network. Moreover, this article uses improved algorithms to extract features from mental health education network data, controls the problems in mental health education in real-time, and proposes targeted improvement strategies. In addition, this article combines the improved algorithm to construct a mental health education system based on big data technology and immunotherapy gene regulation network are combined for an improved algorithm to process mental health education data.Finally, this article verifies the effectiveness of the system through experimental research.

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“…Some mobile technology-based devices even help to manage depression and anxiety. [ 57 ] Young people adapt and learn to use a highly technological, new device more easily compared to older people. Therefore, considering the benefits of using Fitbit and the connection between physical activity and depression, mobile technology-based support to improve physical activity and alleviate depressive symptoms may benefit young women with depression.…”

Section: Discussionmentioning

confidence: 99%

The effects of mobile technology-based support on young women with depressive symptoms: A block randomized controlled trial

Jeong,

Cha,

Nam

et al. 2024

Medicine

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Background: The current body of knowledge highlights the potential role of mobile technology as a medium to deliver support for psychological and physical health. This study evaluated the influence of mobile technology support on depressive symptoms and physical activity in female university students. Methods: A block randomized controlled trial design with a single site was used. Ninety-nine participants were block-randomized into 3 arms: Experimental Group 1 (emotional and informational support group), Experimental Group 2 (informational support group), and the control group. Interventions were delivered via mobile technology for 2 weeks. Data on depressive symptoms and physical activity were collected from 84 participants at baseline and on Days 8 and 15. Data analyses included descriptive statistics, t tests, one-way analysis of variance, and repeated-measures analysis of variance. Results: This study showed no interaction effect of time and group on depressive symptom scores and physical activity, considering the emotional and informational support from mobile technology. However, Experimental Group 1 exhibited a significant reduction in depressive symptoms during the first week of the study compared to Experimental Group 2 and the control group. While physical activity in Experimental Group 2 and control group increased only during the first week of the study and subsequently decreased, Experimental Group 1 showed an initial increase during the first week that was sustained into the second week. Conclusions: Since informational and emotional support showed a strong effect over a short period of time, mobile technology offering emotional support could be used to provide crisis interventions for depression among young women when a short-term impact is required.

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A Machine Learning Implementation for Mental Health Care. Application: Smart Watch for Depression Detection

Cited by 40 publications

References 2 publications

RETRACTED: Diagnosis of depression level using multimodal approaches using deep learning techniques with multiple selective features

RETRACTED: Diagnosis of depression level using multimodal approaches using deep learning techniques with multiple selective features

RETRACTED: Model innovation of students' mental health education from the perspective of big data

The effects of mobile technology-based support on young women with depressive symptoms: A block randomized controlled trial

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