scholarly journals Automatic detection of depression symptoms in twitter using multimodal analysis

2021 ◽  
Author(s):  
Ramin Safa ◽  
Peyman Bayat ◽  
Leila Moghtader
Keyword(s):  
Automatic Detection ◽  
Depression Symptoms ◽  
Multimodal Analysis ◽  
Detection Of Depression

Automatic detection of drowsiness in EEG records based on multimodal analysis

2014 ◽  
Vol 36 (2) ◽  
pp. 244-249 ◽  
Author(s):  
Agustina Garcés Correa ◽  
Lorena Orosco ◽  
Eric Laciar
Keyword(s):  
Automatic Detection ◽  
Multimodal Analysis

scholarly journals Insomnia and depression: Japanese hospital workers questionnaire survey

Open Medicine ◽  
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.

Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis

2011 ◽  
Author(s):  
Douglas Sturim ◽  
Pedro A. Torres-Carrasquillo ◽  
Thomas F. Quatieri ◽  
Nicolas Malyska ◽  
Alan McCree
Keyword(s):  
Factor Analysis ◽  
Automatic Detection ◽  
Gaussian Mixture ◽  
Mixture Modeling ◽  
Gaussian Mixture Modeling ◽  
Detection Of Depression

Latino Farmworkers and Under-Detection of Depression: A Review of the Literature

2018 ◽  
Vol 40 (2) ◽  
pp. 210-226 ◽  
Author(s):  
Francisco J. Limon ◽  
Angela Lamson ◽  
Jennifer Hodgson ◽  
Mark C. Bowler ◽  
Sy Saeed
Keyword(s):  
Primary Care Providers ◽  
Work Conditions ◽  
The United States ◽  
Depression Symptoms ◽  
Health Issues ◽  
Care Providers ◽  
Critical Multiculturalism ◽  
Latino Farmworkers ◽  
History Of ◽  
Detection Of Depression

The history of Latino farmworkers in the United States is long and complex. Economic forces and often-contradictory immigration policies have given rise to patterns of undocumented immigration from Mexico and Central America by individuals and families looking for work. This review uses a Critical Multiculturalism lens to explore the incidence of depression in Latino farmworkers; the link between depression symptoms and the experience of migration, acculturation, poverty, and agricultural work; and the role of culture, structural issues, primary care providers’ inadequate training in mental health issues, and work conditions in the under-detection of depression in this population. Recommendations for clinicians and researchers are offered to improve detection of depression in Latino farmworkers.

scholarly journals Detection of Depression Symptoms Using Chatbots Based on Machine Learning

2020 ◽  
Author(s):  
Vitor Bastos ◽  
André Felipe Monteiro
Keyword(s):  
Machine Learning ◽  
Young People ◽  
Good Accuracy ◽  
Machine Learning Algorithms ◽  
Depression Symptoms ◽  
Complex Task ◽  
Relevant Issue ◽  
High Level ◽  
Initial Results ◽  
Detection Of Depression

Nowadays depression is a relevant issue due the high level of stressobserved even in students and young people. Moreover, the detectionof the depression symptons is a complex task, since eachperson has different behaviors and reactions in these scenarios. Thiswork address the detection of depression symptoms using chatbotsbased on machine learning algorithms. The use of chatbots enablesa smooth approach for shy and introspective people, whose donot feel comfortable for talking to parents, psychologists or medicalprofessionals in general. To this end, an App for smart-phoneis proposed in order to perform a talk with a person, and verifyif some depression symptoms are observed based using machinelearning algorithms. The initial results show that the proposedmodel has a good accuracy on simulated scenarios, where basictalks are performed by the chatbot.

871-P: Integrating the PHQ-A Depression Screening Tool within the Electronic Health Record Increases Detection of Depression Symptoms

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 871-P
Author(s):  
JULIE GETTINGS ◽  
OONA PATIL ◽  
MARY ELLEN VAJRAVELU ◽  
COLIN P. HAWKES ◽  
ALAN TUTTLE ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Screening Tool ◽  
Depression Screening ◽  
Depression Symptoms ◽  
Health Record ◽  
Electronic Health ◽  
Detection Of Depression

Prevalence of Self-Reported Depression Symptoms and Perceived Anxiety Among Community-Dwelling U.S. Adults Reporting Tinnitus

2020 ◽  
Vol 5 (4) ◽  
pp. 959-970
Author(s):  
Kelly M. Reavis ◽  
James A. Henry ◽  
Lynn M. Marshall ◽  
Kathleen F. Carlson
Keyword(s):  
Mental Health ◽  
Fold Increase ◽  
Community Dwelling ◽  
Self Report ◽  
Depression Symptoms ◽  
Community Based Interventions ◽  
Data Set ◽  
Health Distress ◽  
Validated Questionnaire ◽  
Mental Health Distress

Purpose The aim of this study was to examine the relationship between tinnitus and self-reported mental health distress, namely, depression symptoms and perceived anxiety, in adults who participated in the National Health and Nutrition Examinations Survey between 2009 and 2012. A secondary aim was to determine if a history of serving in the military modified the associations between tinnitus and mental health distress. Method This was a cross-sectional study design of a national data set that included 5,550 U.S. community-dwelling adults ages 20 years and older, 12.7% of whom were military Veterans. Bivariable and multivariable logistic regression was used to estimate the association between tinnitus and mental health distress. All measures were based on self-report. Tinnitus and perceived anxiety were each assessed using a single question. Depression symptoms were assessed using the Patient Health Questionnaire, a validated questionnaire. Multivariable regression models were adjusted for key demographic and health factors, including self-reported hearing ability. Results Prevalence of tinnitus was 15%. Compared to adults without tinnitus, adults with tinnitus had a 1.8-fold increase in depression symptoms and a 1.5-fold increase in perceived anxiety after adjusting for potential confounders. Military Veteran status did not modify these observed associations. Conclusions Findings revealed an association between tinnitus and both depression symptoms and perceived anxiety, independent of potential confounders, among both Veterans and non-Veterans. These results suggest, on a population level, that individuals with tinnitus have a greater burden of perceived mental health distress and may benefit from interdisciplinary health care, self-help, and community-based interventions. Supplemental Material https://doi.org/10.23641/asha.12568475

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