Early Detection of Depression Based on Linguistic Metadata Augmented Classifiers Revisited

Depression that stems through social media has been steadily growing since the past few years but with the current inclination towards social media reliance it is highly imperative to detect the early signs. Continuous observation of a user's social media interests and activities may highlight suspicious and negative thoughts. This observation can help in understanding their future course of action and also indicate any suicidal thoughts and behaviors. By using the machine learning models, early indications of depression detection can be addressed. This work studies different word embedding techniques for early detection of depression from social media posts. Further, this work develops a model using various NLP processes in order to address the issue of early detection. The recommendations can be useful as a Decision Support System for counselors, psychologist and also can be of good use by the cyber-crime cell department for criminal investigations.

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Insomnia and depression: Japanese hospital workers questionnaire survey

Open Medicine ◽

10.1515/med-2017-0056 ◽

2017 ◽

Vol 12 (1) ◽

pp. 391-398

Author(s):

Fumihiko Koyama ◽

Takeshi Yoda ◽

Tomohiro Hirao

Keyword(s):

Metabolic Syndrome ◽

Chronic Pain ◽

Early Detection ◽

Wide Spectrum ◽

Chronic Insomnia ◽

Depression Symptoms ◽

Preventative Care ◽

Partial Regression Coefficient ◽

Lifestyle Related Diseases ◽

Detection Of Depression

AbstractObjectivesThis study aimed to identify a correlation between insomnia and the occurrence of depression among Japanese hospital employees using the data obtained from a self-reported questionnaire.MethodsA self-administered questionnaire on sleeping patterns, depression, fatigue, lifestyle-related diseases, and chronic pain was given to 7690 employees aged 20-60 years, and 5,083 employees responded.ResultsAn insomnia score of >2 was observed in 840 (13%) respondents. Chronic insomnia correlated significantly with gender, occupation, overtime work, metabolic syndrome, chronic pain, fatigue, and depression. Moreover, significant negative effects on depression scores were observed in males aged 30-39 (partial regression coefficient: b=0.357, p=0.016), females aged 20-29 (b=0.494, p<0.001), male administrative staff (b=0.475, p=0.003), males with metabolic syndrome (b=0.258, p=0.023), and both genders with chronic insomnia (male; b=0.480, p<0.001: female; b=0.485, p<0.001), and fatigue (male; b=1.180, p<0.001: female; b=1.151, p<0.001).DiscussionInsomnia is a risk factor for depression and for other lifestyle-related diseases. The insomnia score may be useful in preventative care settings because it is associated with a wide spectrum of diseases and serves as a valuable marker for early detection of depression. Thus, our future studies will focus on establishing a method for early detection of depression symptoms among workers across various job profiles.

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THE IMPORTANCE OF EARLY DETECTION OF DEPRESSION IN POST-MASTECTOMY PATIENTS

10.29289/259453942019v29s1ep34 ◽

2019 ◽

Author(s):

Murilo H. C. Silva ◽

◽

Natália S. D. Mendonça ◽

Guilherme A. Sampaio ◽

Laine R. Martins ◽

...

Keyword(s):

Early Detection ◽

Detection Of Depression

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Early Detection of Depression using Screening Tools and Electroencephalogram (EEG) Measurements

International Journal of Integrated Engineering ◽

10.30880/ijie.2020.12.06.025 ◽

2020 ◽

Vol 12 (6) ◽

Author(s):

Wong Hui Ming ◽

◽

Arifah Bahar ◽

Khairul Radhi Bahar ◽

Mitra Mohd.Addi ◽

...

Keyword(s):

Early Detection ◽

Screening Tools ◽

Electroencephalogram Eeg ◽

Detection Of Depression

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Early Detection of Depression in Women using Machine Learning Methods

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1093.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 352-358

Keyword(s):

Machine Learning ◽

Early Detection ◽

Age Groups ◽

World Health Organisation ◽

Scientific Discipline ◽

World Health ◽

Learning Models ◽

Machine Learning Methods ◽

Detection Of Depression ◽

Machine Learning Models

According to World Health Organisation(WHO), most prevailing mental sickness and leading evidence of disability is Depression. In India Depression is much more prevalent in women of all age groups. Eventhough effectual treatment is noted for Depression, it is not reaching the maximum number of sufferers in both wealthy and pathetic countries. In this respect, many scientific discipline and researchers have been employed to develop Machine Learning models to determine level of Depression. This paper presents background knowledge on depression and useage of machine learning and also review past studies that apply machine learning for determine depression with their merits and demerits.

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Health Education Intervention on Increasing Early Detection of Depression Based on Family

Open Access Macedonian Journal of Medical Sciences ◽

10.3889/oamjms.2020.4183 ◽

2020 ◽

Vol 8 (E) ◽

pp. 331-333

Author(s):

Elvi Rosanti ◽

Rizanda Machmud ◽

Adnil Edwin Nurdin ◽

Afrizal Afrizal

Keyword(s):

Health Education ◽

Early Detection ◽

Sampling Technique ◽

Education Intervention ◽

Chi Square ◽

Health Education Intervention ◽

Quasi Experimental ◽

Chi Square Test ◽

Family Based ◽

Detection Of Depression

AIM: The aim of this study was to determine the health education intervention on increasing early detection of depression based on family. METHODS: This study used a quasi-experimental design with one-group pretest-posttest design. The study sample was all family in Solok City, West Sumatera Province, Indonesia, with a sample size of 382 families. The sampling technique used a purposive sampling technique. Health education interventions have been carried out through family-based depression prevention modules that have been validated. Data were analyzed using the Chi-square test and paired sample t-test using the SPSS version 21.0 software. RESULTS: This study showed a statistically significant increase in knowledge, attitude, behavior and early detection of depression after health educational intervention through family based (p < 0.05). CONCLUSION: This study confirmed health education intervention on increasing early detection of depression based on family.

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