Early Detection Of Depression Research Articles

Certainty factor and dempster-shafer method analysis for early detection of final year student depression

Academic loads in writing a thesis cause difficulties to students to complete their studies. This problem can be solved with the application of an expert system with early detection of depressive final-year students that compares Certainty Factor method and Dempster-Shafer method. Certainty Factor method used to measure facts certainty or rules in defining the level of expert confidence, while the Dempster-Shafer method combines uncertainty that has characteristics according to the way of thinking of experts but has a mathematical basis. The purpose of comparing these methods is to get the most appropriate and best method for early detection of depression. The methodology used in this study is a comparative test between two methods, with a mechanism (1) Collecting data related to the depression of final year students; (2) Data acquisition and weighting of confidence values; (3) Calculate formulations from both methods; (4) Perform a comparative test to obtain conclusions. Certainty Factor method is the most appropriate and the best model in early detection of depression, because Certainty Factor method revealed that the more symptoms are given, the higher the probability of detection of depression.

Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences

Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.

The accuracy of depression risk perception in high risk Canadians

BackgroundPrevention and early detection of depression is a top public health priority. Accurate perception of depression risk may play an important role in health behavior change and prevention of depression. However, the way in which people in the community perceive their risk of developing depression is currently unknown. MethodsWe analyzed the baseline data from a randomized controlled trial in 358 men and 356 women who are at high risk of having a major depressive episode (MDE). The predicted risk was assessed by sex-specific multivariable risk predictive algorithms for MDE. We compared participants’ perceived risk and their predicted risk. Accurate risk perception was defined as perceived risk is in the range of predicted risk ± 10%. ResultsIn men, 29.7% perceived their risk accurately; 47.5% overestimated their risk; 22.8% underestimated their risk. In women, the proportions were 21.7%, 59.6% and 18.7%, respectively. Compared to men, women were more likely to overestimate their risk and less likely to be accurate. Regression modeling revealed that poor self-rated health and higher predicted depression risk were associated with inaccuracy of risk perception in men; a family history of MDE, higher psychological distress and lower predicted risk were associated with inaccuracy of risk perception in women. ConclusionsIndividuals who are at high risk of developing depression tend to overestimate their risk, especially women. Inaccurate depression risk perception is related to people's health status. Educational interventions are needed to enhance the accuracy of risk perception to encourage positive behavior change and uptake of preventive strategies.

Uticaj sociodemografskih faktora na pojavu demencije i depresije kod starih osoba

Introduction: The most common mental disorders in the elderly are depression and dementia. Objective: Examine the impact of sociodemographic characteristics on the appearance of depression and dementia in elder patients treated in Primary Health Center (PHC), Banja Luka. Method: The study included 208 patients over 65 years of age, selected by random selection method, registered in 6 family medicine teams at the PHC Banja Luka in the period from March to May 2016. Respondents completed the Sociodemographic Questionnaire, Beck's Depression Inventory, and Folstein Mini-Mental State Examination (MMSE) for dementia assessment. Different statistical procedures were used in the data processing: descriptive analysis in the form of frequencies and percentages, Chi-square test, T-test. The results of the research were statistically analyzed by SPSS 11.5 program. Results: The study included 208 patients older than 65, of whom 38.9% were male. Impaired cognition was found in 7.2% and moderate depression in 24.5% of patients. Dementia was statistically significantly (p = 0.000) more present in the eldest respondents ≥ 81 years, while depression was mostly present in patients aged 76-80. Physically active individuals were significantly less affected by depression (p = 0.001) and dementia (p = 0.000). Dementia and depression occurred more frequently in people living alone and people with a low level of education. Conclusion: A family physician plays an important role in the early detection of depression and dementia in the elderly, the identification of risk factors, prevention, and treatment of these patients.

MGL-CNN: A Hierarchical Posts Representations Model for Identifying Depressed Individuals in Online Forums

More users suffering from depression turn to online forums to express their problems and seek help. In such forums, there is often a large volume of posts with sensitive content, indicating that the user has a risk of suicide and self-harm. Early detection of depression using appropriate deep learning models and social media data can prevent potential self-harm. However, existing depression detection models are not powerful enough to capture critical sentiment information from the large volume of posts published by each user, which makes the performance of these models not satisfying. To address this problem, we propose a hierarchical posts representations model named Multi-Gated LeakyReLU CNN (MGL-CNN) for identifying depressed individuals in online forums. The model consists of two parts: the first one is a post-level operation, which is used to learn the representation of each post of the user, and the second one is a user-level operation, which is used to obtain the overall representation of the user's emotional state. Besides, we propose another depression detection model by changing the number of gated units in the MGL-CNN, which is named Single-Gated LeakyReLU CNN (SGL-CNN). We show how to use our models to identify depressed users through a lot of posted content. Experimental results showed that our models performed better than the previous state-of-the-art models on the Reddit Self-reported Depression Diagnosis dataset, and also performed well on the Early Detection of Depression dataset.

Epilepsy and Depression, Comorbidity Problems of Treatment Tactics

Objective: Despite the availability of literary data on the treatment of comorbidity of epilepsy and depression, their therapy has not been fully resolved. However, epilepsy and depression are often combined. Materials and Methods: The aim of the study was development therapy in the comorbidity of epilepsy and depression in adults. The study included 100 patients with epilepsy. The severity of depression was studied by using the Hamilton scale, ICD-10 and DSM-5 criterions. Patients were observed at the Mental Health Center of the Ministry of Health of the Republic of Azerbaijan from January 2018 to January 2020 for 24 months. Patients took standard antiepileptic drugs (depakin-chrono 1000 mg/day, lamotrigine 150 mg/day, levitracetam 3000 mg/day) and antidepressant drug-citalopram was taken 10 mg twice a day, in the morning and in the evening. Results: Depression of varying severity was detected in 70 % of the examined patients 10 cases of mild, 40 moderate and 20 case of severe depression without psychotic symptoms. Conclusion: The combination of anticonvulsants with antidepressants Citalopram 10 mg in morning and evening (per os) reduces the frequency of seizures and reduces the severity of depression. The study of comorbidity of epilepsy and depression is of great theoretical and practical importance. First, it will contribute to the early detection of depression. Second, prevent suicides. Third, it will help reduce refractory epilepsy. Finally, improve their quality of life for patients with epilepsy.

Early Detection of Depression in Women using Machine Learning Methods

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.

Prevalence and Predictors of Depression Among Medical Residents in Western Saudi Arabia.

There is evidence of a higher depression rate among resident physicians compared to the general population. This study aimed to estimate the prevalence of depression and assess its severity and predictors among physician residents in Jeddah, Western Saudi Arabia. A cross-sectional survey was conducted using the self-administered Patient Health Questionnaire (PHQ-9). This study included 149 medical residents. More than half (53%) were female and 43% were married. A diagnosis of depression (mild to severe) was identified in 75.8% of the participants (mild in 35.6%, moderate in 34.2%, and severe depression in 6.0%). Residents in surgery and emergency programs were at an increased risk for moderate-to-severe depression (OR 2.60, 95% CI 1.13, 5.98; OR 4.90, 95% CI 1.08, 22.20; respectively). Mental health programs for resident physicians would facilitate the early detection of depression and mental disorders. However, adequate mentoring, managing workloads, and social off-duty activities are always vital.

Cooperative Multimodal Approach to Depression Detection in Twitter

The advent of social media has presented a promising new opportunity for the early detection of depression. To do so effectively, there are two challenges to overcome. The first is that textual and visual information must be jointly considered to make accurate inferences about depression. The second challenge is that due to the variety of content types posted by users, it is difficult to extract many of the relevant indicator texts and images. In this work, we propose the use of a novel cooperative multi-agent model to address these challenges. From the historical posts of users, the proposed method can automatically select related indicator texts and images. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods by a large margin (over 30% error reduction). In several experiments and examples, we also verify that the selected posts can successfully indicate user depression, and our model can obtained a robust performance in realistic scenarios.

Validation of the Maria Kovacs Children’s Depression Inventory to Assess Depression among School Children in Afghanistan

Background & aim: Four decades of war in Afghanistan has led to an increased rate of depression among the population. Given the importance of depression in adolescents, it is essential to introduce an appropriate tool for the early detection of depression in adolescents. Therefore, this study was conducted to standardize the Maria Kovacs Questionnaire among schoolchildren in Herat, Afghanistan. Methods: In order to validate the Maria Kovacs children’s depression inventory for the reference group, 57385 adult girls and boys studying in secondary schools in the Herat were selected during 2016. The 500 high school students (grades 7, 8, and 9) were randomly selected and questionnaires were localized by the researcher. Moreover, the reliability of the test was evaluated by means of a mock-up test in split-half format, and Beck's depression inventor was used as gold standard for identifying depression in school children. Results: Using the split-half method, the two inventories of Maria Kovacs and Beck were reported to have the reliability of 0.789 and 0.526, respectively. Moreover, the comparison of Maria Kovacs with Beck inventory (as criterion test) indicated that when the score of non-depression in Kovacs inventory ranged 0-8, it had the sensitivity of 97.2%, specificity of 46.1%, positive, and negative predictive value of 85.7%. Conclusion: According to the obtained sensitivity of the investigated group, Maria Kovacs Depression Inventory has an acceptable level of reliability and validity to be implemented in the target community. So it could be recommended as an appropriate scale to assess depression among school childrenin Afghanistan.

Penanggulangan depresi lansia pascaerupsi Gunung Merapi melalui permainan humor berbasis kearifan budaya lokal pada kader yandu lansia

Mental health of elderly is a main priority which should be concerned of, in order to provide strength to obtain welfare in old age. Merapi eruption was a natural disaster that provided long-term psychological effects on elderly survivors. It is necessary to be handled with continuous support related to depression and life quality of elderly after Merapi eruption by providing training on humor games based on local cultural wisdom and management of depression in elderly in the manner of social spiritual to the cadres of integrated services for elderly in Hunian Tetap (Huntap) Kuwang, Cangkringa, Sleman, Daerah Istimewa Yogyakarta (DIY). Data used in this research was secondary data taken from community service that conducted in May - September 2014. The community service involved 30 cadres of integrated services for elderly and 45 elderly in Huntap Kuwang. Tools used in this research were module of local wisdom games completed with its equipment and module of early detection and coping with depression. The data were presented in descriptive quantitative and qualitative. After being given training on humor games based on local wisdom, there were increased average score on cadres’ knowledge of humor games based on local wisdom (from 66.4 to 93.2.) and skill (from 64.1 to 87.6,) as well as knowledge of early detection of depression (from 47.6 to 84.4) and skill of social support providing (from 35.3 to 80.8). There were declining trend of depression in elderly from 36 people before training to 17 people after the training of humor games based on local wisdom and spiritual social support. Training on humor games based on local wisdom and early detection of depressive tendencies along with the coping to the cadres of integrated services can reduce depressive tendencies and improve life quality of elderly in Huntap Kuwang, Cangkringan, Sleman, DIY.

Early Detection of Depression: Social Network Analysis and Random Forest Techniques.

BackgroundMajor depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder.ObjectiveThis study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span.MethodsWe proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities.ResultsThe evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%.ConclusionsGiven the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.

Mediating Effects of Social Support on Depression and Suicidal Ideation in Older Korean Adults With Hypertension Who Live Alone.

ABSTRACTBackground:Older adults who live alone are less physically and emotionally healthy and report higher levels of depression relative to those who do not live alone. Suicide is the most problematic health issue reported by older adults who live alone. In particular, vulnerable older adults who live alone experience difficulty obtaining self-care and medical services; therefore, early detection of depression is difficult, and there are few opportunities to implement suicide prevention strategies in this population. In addition, social support for depression is an important factor affecting illness and economic vulnerability in older adults who live alone.Purpose:This study aimed to examine the relationship between depression and suicidal ideation in vulnerable older Korean adults with hypertension and to explore the mediating effect of social support on this relationship.Methods:The study used a cross-sectional design. Vulnerable older adults who were 65 years old or older with hypertension and who received home visit services from a public health center were invited to participate. The participants completed structured questionnaires, including the 15-item Short-Form Geriatric Depression Scale, 12-item Multidimensional Scale of Perceived Social Support, and 19-item Scale for Suicidal Ideation, and provided information regarding their demographic characteristics, health status, and economic status. Descriptive and correlation analyses were performed to examine the correlations among these variables. The three-step regression analysis method proposed by Baron and Kenny was used to examine the mediating effect of social support.Results:The mean depression, social support, and suicidal ideation scores of participants were 23.64 (± 2.04), 35.94 (± 15.40), and 7.80 (± 7.73), respectively. In addition, depression was negatively correlated with social support (r = −.27) and positively correlated with suicidal ideation (r = .21), whereas social support was negatively correlated with suicidal ideation (r = −.35). Social support mediated the relationship between depression and suicidal ideation (Z = 2.69).Conclusions:Social support was identified as an important variable for older adults with chronic illness who lived alone. Interventions that include social support hold the potential to reduce depression and suicidal ideation in this population.

Depressive symptoms in adolescents in Kuching, Malaysia: Prevalence and associated factors.

The trend of depression is rising worldwide. There are limited studies on depression in adolescents. The aim of this study was to estimate the prevalence of and identify the factors associated with depressive symptoms in adolescents in Kuching, Malaysia, and to analyze the relationship between depressive symptoms and suicidal ideation. A cross-sectional study was conducted in 320 students from urban and rural secondary schools. The participants were randomly selected via multi-stage sampling. They completed the Malay versions of the Children's Depression Inventory (CDI) and Beck Scale for Suicide Ideation (BSS). The prevalence of depressive symptoms was 26.2%. On binary logistic regression analysis, variables with significant association with depressive symptoms were race (P = 0.028), type of class (P < 0.001), mother's education level (P = 0.036), type of housing (P = 0.036), parents' marital status (P = 0.012), alcohol intake (P = 0.005), stealing (P < 0.001) and history of disciplinary record (P = 0.005). Variables that remained significant on multivariable logistic regression were type of class (P = 0.004), parents' marital status (P = 0.017) and stealing (P < 0.001). Students from the Arts stream (OR, 2.43) with parents who were separated, divorced or widowed (OR, 3.13) and who had experience of stealing (OR, 3.27) were predicted to be at risk of developing depressive symptoms. There was a significant correlation between total CDI score and total BSS score (P < 0.001). The prevalence of depressive symptoms in adolescents was high. Depressive symptoms are significantly correlated with suicidal ideation. Greater collaboration between the education and health agencies is essential for mental health promotion in schools and early detection of depression, especially in at-risk adolescents.

Chronic Stress Disturbs Metabolome of Blood Plasma and Urine in Diabetic Rats.

About 30% of diabetes patients suffer from varying degrees of depression. Diabetes itself is associated with abnormal carbohydrate and energy metabolism. Whether chronic stress-induced depression-like behavior impacts the metabolome of blood plasma and urine in diabetes is not clear. This study aimed to investigate the effect of chronic stress on metabolome of plasma and urine in spontaneously diabetic Goto-Kakizaki (GK) rats. The GK rats were subjected to 8 weeks' chronic unpredictable mild stress (CUMS) to induce depression-like behavior. Metabolome analysis of blood plasma and urine using liquid chromatography mass spectrometry (LC/MS) was performed. Multivariate data analysis was used to evaluate the data. Behavior and biochemical assay confirmed the successful establishment of CUMS-induced depression-like behavior model in rats. Disturbance of 20 plasma metabolites and 16 urine metabolites were altered in CUMS-induced depression GK rats as compared to control ones. These disturbed metabolites were involved in fatty acid metabolism, sphingolipid metabolism, phenylalanine metabolism, citrate cycle, glycolysis, glutathione metabolism, and nicotinate and nicotinamide metabolism. This study suggest that chronic stress-induced depression-like behavior may further disturb diabetes-itself energy metabolome. The plasma and urine lipid metabolites monitoring may be useful for early detection of depression in patients with diabetes mellitus.

Older people living alone in Shanghai: A questionnaire survey of their life experience.

Globally the number of older people living alone is increasing. Little is known about the life experience of older people living alone in Mainland China. This study aimed to explore older people's experience regarding different components of their lives. A cross-sectional survey of 387 older people aged 60years and above and living alone in two communities in Shanghai was conducted from April to July 2015. A structured questionnaire including 15-item Geriatric Depression Scale, Activity of Daily Living Scale, UCLA Loneliness Scale version 3, Social Support Rate Scale and Older People's Quality of Life Questionnaire was used to assess the health status, loneliness, social support, quality of life and demographic information. A total of 15.7% of the participants rated their health as poor with 56.8% reporting chronic diseases and 26.9% reporting being depressed. A total of 71.1% of the participants reported a high level of functional ability. However, 54.3% and 21.7% of the participants reported a moderately and moderately high level of loneliness respectively. The median of SSRS was 30 and the mean of Older People's Quality of Life Questionnaire was 120.2, indicating a lower level of social support and quality of life. There were statistically significant differences in health, loneliness, social support and quality of life across the participants with different characteristics. Interventions to improve the health status, reduce loneliness, increase social support and maintain or improve quality of life of older people living alone in Shanghai could be developed and implemented. Potential interventions include providing frequent home care services, early detection of depression, encouraging more contacts from children and other family members, and providing support from other sources.

Increased sensitivity to sad faces in depressive symptomatology: A longitudinal study

BackgroundNegative bias to sad emotional stimuli has repeatedly been identified as a cognitive feature of major depressive disorder, particularly in facial expression recognition. However, it is unknown how negative bias applies to facial expression recognition in individuals with depressive symptoms. MethodsA total of 58 university students’ facial expression recognition and depression scores were measured using facial stimuli displaying the whole face, with 39 participants completing a 3-month follow-up assessment of depressive symptoms. We used an index that differentiates sensitivity to sadness from sensitivity to general facial expressions. Participants gave written informed consent after a complete description of the study. ResultsThe group with higher depressive symptoms showed a higher sensitivity to sadness than those with lower depressive symptoms, indicating a relationship between negative bias in facial expression recognition and nonclinical depressive symptoms. From a longitudinal point of view, negative bias to sad expressions at one time point was found to predict the change in depression scores at the 3-month follow-up. LimitationsSince the participants included only college students, the generalizability of our results to other populations is limited. The three-month interval may not be sufficient to determine the prognosis of depression, suggesting the need for further replications. DiscussionThis study was the first to report the negative bias of facial expression recognition in individuals with nonclinical depressive symptoms. Given that negative bias predicts present and future depression, facial expression recognition has the potential to be used as a screening tool for early detection of depression.

Walking ability, anxiety and depression, significantly decrease EuroQol 5-Dimension 5-Level scores in older hemodialysis patients in Japan

BackgroundHemodialysis patients in Japan are aging, but the influence of lifestyle factors on EuroQoL 5-Dimension 5-Level (EQ-5D-5 L) scores in older hemodialysis patients is unclear. This study aimed to measure health-related quality of life using the EQ-5D-5 L for older hemodialysis patients and to identify the lifestyle factors affecting EQ-5D-5 L scores in Japan. MethodsOutpatients aged 65 years or older who had been receiving maintenance hemodialysis for more than 5 years were selected to determine their EQ-5D-5 L scores. Multiple linear regression analysis was applied to identify items affecting the EQ-5D-5 L scores. ResultsThe response rate was 59.7% (748/1251), after excluding 9 patients who did not fulfill the participation criteria, and we analyzed data from 739 patients (462 men, 277 women) aged 72.9 ± 6.5 years (mean ± standard deviation) with a mean duration of hemodialysis of 15.1 ± 8.8 years. The mean EQ-5D-5 L score was 0.738 ± 0.207. Higher Hospital Anxiety and Depression Scale scores (regression coefficient, −0.467; P < 0.001), reduced walking ability (−0.201; P < 0.001), duration of hemodialysis (−0.175; P < 0.001), age (−0.143; P < 0.001), and subjective symptoms (−0.128; P < 0.001) showed significant negative correlations with the EQ-5D-5 L score. ConclusionsReduced walking ability, and Anxiety and Depression, were associated with lower EQ-5D-5 L score and appeared to be important factors in decreasing health for older hemodialysis patients. These results suggest that patients’ families and dialysis facility staff should be trained in early detection of depression and that patients should maintain their walking ability by exercise therapy.

Brain Wave Frequency Measurement in Gamma Wave Range for Accurate and Early Detection of Depression

The Global System of Mobile Communication (GSM) which debuted in Nigeria in August 2001 was greeted with much celebration as the country finally joined the League of the GSM nations, though after less economically buoyant African countries such as Botswana, Mozambique, Uganda and Tanzania. Fourteen years after the introduction, Issues and challenges has been raised on why subscribers are been ripped of their money and short changed with epileptic services. This arises from the myriad of problems ranging from congestion to the poor network delivery. This paper discusses the evolution of GSM services in Nigeria, the range of services available and the issues involved in its operation in the last fourteen years. Not forgetting the benefit it has brought to the Nigerian economy. Suggestions are also made on how Nigeria can reap more benefits of the GSM communication.