Готові списки джерел за темами / Depression analysis

Добірка наукової літератури з теми "Depression analysis"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Depression analysis"

1

Sharma, Vandana. "Prevalence of Depression Among Adolescents: A Comparative Analysis." Paripex - Indian Journal Of Research 3, no. 6 (January 15, 2012): 53–55. http://dx.doi.org/10.15373/22501991/june2014/17.

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2

Kharytonov, Volodymyr. "Analysis of the emotional state of patients with epilepsy and depression in interactal period." Ukrains'kyi Visnyk Psykhonevrolohii 27, no. 3 (September 5, 2019): 53–56. http://dx.doi.org/10.36927/2079-0325-v27-is3-2019-8.

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The paper presents the results of the analysis of the emotional state of patients with epilepsy and depressions in the interictal period. Was analyzed the entire spectrum of aff ective manifestations (positive and negative emotional reactions), and were separately investigated the structural features of anxiety. It has been established that in the structure of the emotional state of patients with epilepsy, both with depressions and without, is noted the presence of negative aff ectivity in the form of reactions of tension, anxiety, anger, and frustration. The specifi c character of the emotional state of patients with epilepsy with depressions lies not so much in the representation of specifi c negative emotional states, but in the range of their severity. The emotional state of patients with epilepsy and depressions is represented by extremely negative emotional reactions, among which the most pronounced and characteristic of all forms of depression is the feeling of sadness. The specifics of the emotional state are determined depending on the form of depression: against the background of the state of sadness, with organic depression in the aff ective state, dominates the feeling of fatigue; with psychogenic — tension and anxiety; with endogenous — self-deprecation. Were distinguished the structural features of anxiety in patients with various forms of depression, was noted its high level in psychogenic depressions.
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3

Girard, Jeffrey M., and Jeffrey F. Cohn. "Automated audiovisual depression analysis." Current Opinion in Psychology 4 (August 2015): 75–79. http://dx.doi.org/10.1016/j.copsyc.2014.12.010.

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4

Cecchi, F., G. Giacalone, and G. Paci. "Inbreeding depression in the Lizard canary breed estimated by pedigree analysis." Czech Journal of Animal Science 61, No. 1 (July 15, 2016): 15–21. http://dx.doi.org/10.17221/8664-cjas.

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5

Dong, Min, Jia Fu Qi, Hui Dong, Hong Li, and Xiao Long Chen. "Application of Balanced Cross Section on the Basin Analysis for Cenozoic of Huanghua Depression." Advanced Materials Research 518-523 (May 2012): 5636–39. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5636.

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In this paper, the seismic profiles of the central area of Huanghua depression were balanced and restored by 2DMove software. We calculated the extension parameters of the balanced corss-section in the different period, analyzed the tectonic deformation of Huanghua depression. According to the total extension parameters of balanced cross-section, the Cenozoic evolution of the Huanghua depression may be divided into two phase of tectonic evolution which are rifting stage and post-rift stage. The rifting stage included three episodic rifting: The third members of Shehejie with the rapidest subsidence,Ⅰepisodic rifting; The first and second members of Shahejie with the fault-depression,Ⅱepisodic rifting; The Dongying with terminal fault-depressed ,Ⅲepisodic rifting . The Huanghua depression shows a double-layer vertical structure with faulting structures in the lower and depressing structure in the upper sector.
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6

Mantri, Prof Shamla, Dr Pankaj Agrawal, Prof Dipti Patil, and Dr V. M. Wadhai. "Depression Analysis using ECG Signal." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 7 (November 17, 2013): 2746–51. http://dx.doi.org/10.24297/ijct.v11i7.3470.

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ECG is a bio-medical signal which records the electrical activity of the heart versus time. They are important for diagnostic and research purposes of the human heart. In this paper we discuss a method of feature extraction which is an inevitable step in most approaches in diagnosing abnormalities in the heart. A web application is developed which extracts features of ECG signal like ST segment, QRS wave, etc. and use these features for identifying whether a person suffers from any of the four levels of stress, that is, Hyper Acute stress (Myocardial Infarction), Acute stress (Type A), Hyper Chronic stress (Ischemia) or Chronic Stress (Type B). The application is built using a decision support system formed by extensive learning of behavior of the signals of various persons.Â
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7

Almutairi, Hessa, and Kholoud Alharbi. "Postpartum Depression Detection: Concept Analysis." Saudi Journal of Nursing and Health Care 5, no. 5 (May 9, 2022): 99–104. http://dx.doi.org/10.36348/sjnhc.2022.v05i05.002.

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Postpartum depression is one of the health problems that may occur without being recognized which may lead to a serious adverse effect for mother and her child. Postpartum depression remain underestimated and unrecognized as many women are discharged without being detected as having PPD or even uneducated in order to seek for help when needed. Postpartum depression has a negative impact on both mother and the child wellbeing. Early detection is essential for mothers’ and infants’ safety. Postpartum depression is defined as “a disorder that is often unrecognized and undertreated”. The aim of this paper is to go through an analysis for postpartum depression detection and get a clear picture about the definition, attributes, antecedents, consequences and its empirical References. Method: a review of the literature was conducted. Articles from 2013 to 2020 were included. Walker and Avant framework was used for concept analysis. Results: the search have found four attributes: time, risk factors, symptoms, and outcome. Also, the search have found the following antecedents: knowledge, screening and care. The consequences are access to mental health care and drawing the treatment line process. Conclusion: this analysis will help in discovering the suitable methods for detecting the postpartum depression to achieve high quality care for mothers and infants.
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8

Goldbort, Joanne. "Transcultural Analysis of Postpartum Depression." MCN, The American Journal of Maternal/Child Nursing 31, no. 2 (March 2006): 121–26. http://dx.doi.org/10.1097/00005721-200603000-00012.

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9

Huys, Quentin J. M., Nathaniel D. Daw, and Peter Dayan. "Depression: A Decision-Theoretic Analysis." Annual Review of Neuroscience 38, no. 1 (July 8, 2015): 1–23. http://dx.doi.org/10.1146/annurev-neuro-071714-033928.

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10

Levy, Valerie, and Paul Kline. "Perinatal depression: a factor analysis." British Journal of Midwifery 2, no. 4 (April 2, 1994): 154–59. http://dx.doi.org/10.12968/bjom.1994.2.4.154.

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Дисертації з теми "Depression analysis"

1

Sobocki, Patrik. "Health economics of depression /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-897-5/.

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2

Zhu, M. (Mo). "Sentiment analysis on medical treatment of depression." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201611103002.

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Applying ICT approach to contribute to improving human’s life is a good purpose for researchers (Pannel, 1993). Thus, implementing products or services with new techniques can be quite an interesting and meaningful topic. Nature language processing is a mature technique may apply machine learning and this technique has already been applied to many applications to server people such as Siri, Chatbot and Google Now. Sentiment analysis is a subject in nature language processing, however, it has not been applied to many fields in our daily life. Depression is a mental disease which caused a lot of trouble in sociality (Hamilton,1960). It causes a huge damage in people’s daily life in the mental aspect instead of the physical painful. And it is also proved by (Help, 2013) that this kind of mental causing a lot of troubles to patients. Quite many researchers are working to find some better treatments for it. However, similar to any other mental disease, there are many treatments existing for different patients and finding the best treatment for a patient can be a quite difficult job. Thus, in this research, I’m trying to validate the function of the sentiment analysis system by applying the data about depression. In order to achieve this problem, I have defined three research questions which lead to solving this problem. (1) What is the best algorithm for implementing the sentiment analysis system? (2) What is the best existing sentiment lexicon library which can be applied to implement the sentiment analyzing system? (3) How to implement the sentiment analysis system with a selected sentiment lexicon library? During the research process, I review the literature which is related to these questions to find out the answers. After all, I selected an open source the sentiment lexical library named SentiWordNot3.0 which was implemented based on an algorithm has the feature of Kth-Nearest Neighbor algorithm and Support Vector Machine. And it proved that following approach can be actually used in sentiment analysis in the medical domain.
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3

Lohman, Matthew. "Frailty and Depression: A Latent Trait Analysis." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3324.

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Background: Frailty, a state indicating vulnerability to poor health outcomes, is a common condition in later life. However, research and intervention progress is hindered by the current lack of a consensus frailty definition and poor understanding of relationships between frailty and depression. Objectives: The goal of this research is to understand the interrelationships between frailty and depression among older adults. Specifically, this project aims 1) to examine the construct overlap between depression and three definitions of frailty (biological syndrome, medical burdens, and functional domains), 2) to determine the degree to which this overlap varies by age, gender, race/ethnicity and other individual characteristics, 3) to evaluate how the association between frailty and depression influences prediction of adverse health outcomes. Methods: This project uses data from the 2004-2012 Health and Retirement Study (HRS), an ongoing, nationally-representative cohort study of adults over the age of 55. Frailty was indexed by three alternative conceptual models: 1) biological syndrome, 2) cumulative medical burdens, and 3) functional domains. Depressive symptoms were indexed by the 8-item Center for Epidemiologic Studies Depression (CESD) scale. Latent class analysis and confirmatory factor analysis were used to assess the construct overlap between depressive symptoms and frailty. Latent growth curve modeling were used to evaluate associations between frailty and depression, and to estimate their joint influence on two adverse health outcomes: nursing home admission and falls. Results: The measurement overlap of frailty and depression was high using a categorical latent variable approach. Approximately 73% of individuals with severe depressive symptoms, and 85% of individuals with primarily somatic depressive symptoms, were categorized as concurrently frail. When modeled as continuous latent factors, each of the three frailty latent factors was significantly correlated with depression: biological syndrome (ρ = .67, p <.01); functional domains (ρ = .70, p <.01); and medical burdens (ρ = .62, p <.01). Higher latent frailty trajectories were associated with higher likelihood of experiencing nursing home admission and serious falls. This association with adverse health outcomes was attenuated after adjustment for depression as a time-varying covariate. Conclusions: Findings suggest that frailty and frailty trajectories are potentially important indicators of vulnerability to adverse health outcomes. Future investigations of frailty syndrome, however it is operationalized, should account for its substantial association with depression in order to develop more accurate measurement and effective treatment.
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4

Suzuki, Tsudoi. "Comparative analysis of depression in homeless populations." CSUSB ScholarWorks, 2003. https://scholarworks.lib.csusb.edu/etd-project/2393.

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The purpose of this project was to find out whether or not there were specific factors that made homeless people depressed. Finding depression factors will help to establish strategies and programs that would prevent homeless people from being depressed. The current study tested the hypothesis that basic needs and education are needed to help in factoring depression.
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5

Abd, Yusof Noor Fazilla. "Computational approaches to depression analysis : from detection to intention analysis." Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=238393.

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The proliferation of social media-based research on mental health offers exciting possibilities to complement traditional methods in mental health care. As ascertained by psychology experts, the online platform should get priority over offline as it offers considerably reliable diagnosis than granted in person. Early detection does not only alleviate the effects of depression on the patient but also benefits the whole community. In this thesis, we explore computational methods in tackling some of the research challenges in depression analysis and make four contributions to the body of knowledge. First, we develop a binary classification model for classifying depression-indicative text from social media. We propose three feature engineering strategies and assess the effectiveness of supervised model to enhance the classification performance in predicting posts indicate depression. To tackle the short and sparse social media data, we particularly integrate the coherent sentiment-topic extracted from the topic model. Additionally, we propose strategies to investigate the effectiveness of affective lexicon in the task of depression classification. Second, we propose a computational method for analysing potential causes of depression from text. With this study, we demonstrate the ability to employ the topic model to discover the potential factors that might lead to depression. We show the most prominent causes and how it evolved over time. Furthermore, we highlight some differences in causes triggered between two different groups, i.e. high-risk of depression and low-risk. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment. Third, we develop a computational method for monitoring the psychotherapy outcome from the individual psychotherapy counselling. Third, we develop a computational method for monitoring the psychotherapy outcome from the individual psychotherapy counselling. By doing this, we show the possibilities of utilising the topic model to track the treatment progress of each patient by assessing the sentiment and topic discussed throughout the course of psychotherapy treatment. Fourth, we propose an unsupervised method called split over-training for identifying user's intention expressed in social media text. We develop a binary classification model for classifying intentions in texts. With this study, we want to show the possibility of applying the intention analysis in mental health domain. Overall, we demonstrate how computational analysis can be fully utilised to benefit clinical settings in mental health analysis. We suggest that more future work could be further explored to complement the traditional settings in mental health care.
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6

Blackhurst, Zachary Joseph. "Predictors of Paternal Postpartum Depression: A Meta-Analysis." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/9211.

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While major depression is a well-defined mental health condition, it's presentation can be vastly different across individuals and groups, and many factors impact one's vulnerability. A critical period of vulnerability to depression is the postpartum period. Despite the frequent assumption that postpartum conditions solely affect women, prevalence estimates of paternal postpartum depression (PPPD) are comparable with those of women. Similar to mothers, men experience significant biological and psychosocial changes following the birth of a child that contribute to the onset of postpartum depression. A growing body of research has identified many potential variables related to PPPD, yet there is no clear consensus as to which variables are the most impactful. Following PRISMA guidelines, we conducted a meta-analysis to aggregate the known findings and quantitatively determine the most salient predictors of PPPD. We hypothesized that the psychosocial factors of relationship satisfaction, social support, partner depression and history of depression would yield the strongest aggregate effect sizes. By searching databases using logical operations, as well as conducting backward and forward searches, we identified 129 articles that met inclusion/exclusion criteria with a total sample size of 114,712 participants (79,743 fathers). Trained independent coders extracted data directly from identified studies, including sample characteristics for descriptives and moderators, and demographic correlates. Correlates of primary interest include but are not limited to maternal depression, history of depression, relationship satisfaction, and social support. Effect sizes represented as Pearson correlations (or transformed to r using standard procedures) were calculated and compared using a random-effects model. In partial support of our hypothesis, fathers' reported history of depression had one of the largest effect sizes (r = .469, unbiased r = .368). The other primary psychosocial factors had relatively moderate effect sizes: social support, r = -.359 (unbiased r = -.398); relationship satisfaction, r = -.300 (unbiased r = -.266); and partner depression, r = .294 (unbiased r = .471). The correlates of PPPD with the strongest effect sizes were all factors associated with personal distress and stressful pregnancies. Demographic correlates (i.e., younger age, non-White ethnicity, lower education, unemployment, lower SES) generally had small though statistically significant effect sizes. This study helps provide a clearer picture of what predicts PPPD to more accurately screen for men at greatest risk and inform the development of diagnostic measures and treatments. It also lays the groundwork for future work in a more unified and productive direction. By researching and sharing better information, we can provide better access and better outcomes for postpartum couples.
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7

Stewart, Matthew, and John Regan. "Meta-Analysis: Pharmacological Treatment of Depression in Advanced Cancer." The University of Arizona, 2013. http://hdl.handle.net/10150/614293.

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Class of 2013 Abstract
Specific Aims: To evaluate efficacy of the current pharmacological treatment of depression in the adult advanced and terminal cancer patient population. Methods: Trials assessing a pharmacological treatment for depression in cancer patients were found using MEDLINE and PSYCINFO databases. Comprehensive Meta-Analysis software was used to generate a random effects model forest plot, a funnel plot, classical fail-safe N, I2, and Kendall’s tau. Main Results: Ten studies, with an aggregate population was 1,167 patients, were used in this meta-analysis to generate a random effects variance model. The effect size was 0.42 +/- 0.09 (p < 0.01). I2 for aggregate data was 66.16 (p < 0.01). Kendall’s tau with continuity correction was 0.272 (P-value [2-tailed] = .244). The classic fail-safe N was 151 (p < 0.1). Three studies reported a significant increase in adverse effects between treatment and comparison group. Conclusion: Antidepressants were shown to have a moderate effect size when treating depression in advanced and terminal cancer patients. These medications were well tolerated. Antidepressant medications are beneficial as part of a comprehensive treatment plan for cancer patients diagnosed with depression.
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8

Sutton, Joyce. "Depression in Caregivers of Alzheimer’s Patients: Concurrent Validity of Two Depression Scales." TopSCHOLAR®, 1987. https://digitalcommons.wku.edu/theses/2066.

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Forty-five male and female family caregivers of institutionalized and noninstitutionalized Alzheimer’s Disease patients were assessed for depression, using the Beck Depression Inventory (BDI) and Geriatric Depression Scale (GDS). Internal consistency and concurrent validity for the BDI and GDS were acceptable, based on item-total correlation, coefficient alphas, and a Pearson’s Product-moment correlation. A stepwise multiple regression analysis was carried out to determine which variables predict depression in Alzheimer’s caregivers. Using the traditional cut-off score of 11 for each scale, it was found that 42% were depressed on the BDI, while 58% were depressed on the GDS; these rates were not significantly different. No difference in depression rate was found between male and female caregivers or between spouse and adult daughter caregivers. However, the depression rate for the caregiver sample was significantly higher than that reported for the general population of older adults. The regression analysis showed that only two variables were predictive of depression in this group of Alzheimer’s caregiver. The less frequently the caregiver spent time away from the patient, the less frequently the caregiver had help in caring for the patient, the more likely the caregiver was to be depressed. It is suggested that social support may be an important factor in the prevention and/or alleviation of depression in family caregivers of patients with Alzheimer’s Disease. Future research is suggested including validation of the BDI and GDS for Alzheimer’s caregivers, using a large representative sample.
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9

Kay, Sheran Maria. "An analysis of memory loss in dementia and depression." Thesis, University of Canterbury. Department of Psychology, 1990. http://hdl.handle.net/10092/3978.

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Previous studies comparing memory loss in dementia and depression have been criticised for failing to equate for differences in initial learning ability. The purpose of this study was to see if elderly depressed subjects would demonstrate a normal "U" shaped serial position curve (i.e. the tendency to show increased recall of items at the beginnings and endings of lists) after their level of recall was reduced to the same level as subjects with dementia by decreasing the time to study each item. Groups of normal (N=20), depressed (N=20) and dementing (N=20) subjects were examined for their ability to remember serially presented words and pictures. The results of analyses revealed that while depressed subjects showed significant memory loss, pattern of memory recall clearly distinguished them from the demented group. In comparison to the dementia sample, depressives continued to show a robust "primacy effect" (i.e. better recall of words from the beginnings of lists). Results were consistent for both visual and verbal modalities. This finding is in accord with previous research suggesting that memo,ry loss experienced in depression is characteristically different from that found in dementia. The serial position paradigm may be clinically useful for distinguishing patients with early dementia from elderly depressed patients with memory deficits.
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10

NGUYEN, ERIK. "OREGON: ANALYSIS OF OREGON’S RESPONSE TO THE GREAT DEPRESSION." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613370.

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The following paper details some of the most important reactions the State of Oregon had to the Great Depression during the 1930s.The majority of the work available today on the Great Depression deals with what the federal government did to help the states, but seldom is there information on what the states did themselves. Professor Price Fishback, of the University of Arizona, has provided his honors students with an opportunity to work on a project that has never been done before. With the data we now have, we are able to talk about what the states did during the Great Depression and back it up with hard evidence. By looking through state statutes and newspapers from the 1930s, I was able to construct a narrative describing Oregon during that time. A regression analysis is also included to find the effects of five variables on state tax revenue per capita and I was able to compare these variables with Oregon’s surrounding states to see how they all fared in comparison. The thesis paper provides us with greater insight to the State of Oregon during the Great Depression as well as a better understanding to the data that we currently have.
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Книги з теми "Depression analysis"

1

Johansson, Eva L. Diagnosis of depressive disorders: Subject analysis index with reference bibliography. Washington, D.C: Abbe Publishers Association, 1987.

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2

Hsiao, I.-Hsin. A Sociological Analysis of Depression in China. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6471-0.

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3

Galasiński, Dariusz. Men's discourses of depression. New York: Palgrave Macmillan, 2008.

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4

Klassifikatorische Aspekte depressiver Störungen. Frankfurt am Main: P. Lang, 1988.

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5

Prosperity and depression: A theoretical analysis of cyclical movements. New Brunswick: Transaction Publishers, 2011.

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6

Holding and interpretation: Fragment of an analysis. London: Hogarth Press, 1986.

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7

The great depression of 1990. Dallas, Tex: Venus Books, 1985.

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8

Batra, Raveendra N. The great depression of 1990. Toronto: Bantam, 1988.

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9

Batra, Raveendra N. The great depression of 1990. New York, N.Y: Dell, 1988.

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10

Batra, Raveendra N. The great depression of 1990. New York: Simon & Schuster, 1987.

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Частини книг з теми "Depression analysis"

1

Tokuno, Shinichi. "Pathophysiological Voice Analysis for Diagnosis and Monitoring of Depression." In Understanding Depression, 83–95. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6577-4_6.

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2

Hautzinger, Martin, Martin Härter, and Elisabeth Schramm. "Cognitive Behavioural Analysis System of Psychotherapy bei chronischer Depression." In Chronische Depression, 325–32. Göttingen: Vandenhoeck & Ruprecht, 2013. http://dx.doi.org/10.13109/9783666451683.325.

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3

Galasiński, Dariusz. "Men, Depression and Discourse Analysis." In Men's Discourses of Depression, 1–22. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/9780230227620_1.

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4

Röttgers, Hans-Onno, and Carsten Konrad. "Praxis des Cognitive Behavioral Analysis System of Psychotherapy (CBASP)." In Therapie der Depression, 117–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-50347-8_7.

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5

Deshpande, Yash, Shreya Patel, Meghan Lendhe, Manpreet Chavan, and Reeta Koshy. "Emotion and Depression Detection from Speech." In ICT Analysis and Applications, 257–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8354-4_27.

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6

Nguyen, Nguyet Thanh, and Henrik Kalisch. "The Stability of Solitary Waves of Depression." In Analysis and Mathematical Physics, 441–53. Basel: Birkhäuser Basel, 2009. http://dx.doi.org/10.1007/978-3-7643-9906-1_22.

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7

Gottschalk, Louis A., and Julia Hoigaard. "A Depression Scale Applicable to Verbal Samples." In Content Analysis of Verbal Behavior, 105–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-71085-8_8.

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8

Bachmann, M., A. Suhhova, J. Lass, K. Aadamsoo, Ü. Võhma, and H. Hinrikus. "Detrended Fluctuation Analysis of EEG in Depression." In IFMBE Proceedings, 694–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_172.

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Nor, Norma Mohamad, Noorihan Abdul Rahman, Mohd Ridzwan Yaakub, and Zuriani Ahmad Zukarnain. "Sentiment Analysis on Depression Detection: A Review." In Lecture Notes in Networks and Systems, 718–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10464-0_48.

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Chordia, Aishwarya, Mihir Kale, Mukta Mayee, Preksha Yadav, and Suhasini Itkar. "Automatic Depression Level Analysis Using Audiovisual Modality." In Smart Computing Techniques and Applications, 425–39. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1502-3_43.

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Тези доповідей конференцій з теми "Depression analysis"

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Joshi, Jyoti. "Depression analysis." In the 14th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2388676.2388747.

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Innocencio, Giovanna de Camargo, Paulo Roberto Hernandes Júnior, Patrick de Abreu Cunha Lopes, Juliana de Souza Rosa, and Jhoney Francieis Feitosa. "Epidemiological analysis, risk factors and therapeutic plan for post-stroke depression." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.176.

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Анотація:
Background: the stroke is defined by the OMS as the rapid development of neurological symptoms and/or focal signs that last for more than 24 hours, resulting from the sudden change in blood flow to the region. Major depressive disorder is one of the main complications that exist after a stroke. Objectives to correlate the occurrence of depression and stroke, to analyze the risk factors and the best therapeutic approach for the condition. Methods: a literature review was carried out from the Scielo and PubMed database, using as descriptors “Stroke”, “Depression” and “Post-stroke depression”, where 13 articles between 2003 and 2018 were selected. Results: the major depressive disorder is the most common psychiatric complication after strokes. A meta-analysis identified a cumulative incidence of depression from 29% to 52% in the first five years after stroke, although several studies have shown that post-stroke depression is diagnosed in only 10% of cases. When not diagnosed or treated, it is associated with a reduction in the patient’s active participation in the rehabilitation process, a decrease in quality of life and an increase in mortality. Risk factors include previous functional and cognitive impairment, history of depressive disorder, sex, age, previous stroke, hypercortisolemia, poor social support network, neuroanatomical characteristics of the stroke and high serum levels of IL-6. The pharmacological management can be carried out prophylactically or therapeutically, with selective serotonin reuptake inhibitors being the most indicated and tricyclic antidepressants as an alternative. Conclusion: the frequency of depressive disorder after stroke is relatively high and characterized as a predictor of poor prognosis. The importance of attention to the multifactorial context in which depression arises and the early treatment of psychiatric comorbidities in post-stroke individuals should be reinforced, since this strategy may reflect on better quality of life and reduction in morbidity and mortality rates that occur after the condition.
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Salsabilla, Dinda Anindita, Hanung Prasetya, and Bhisma Murti. "The Effect of Unplanned Pregnancy on Antenatal Depression: A Meta-Analysis." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.116.

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ABSTRACT Background: Family planning is a potential strategy for the prevention of postpartum depression. Women who report unintended pregnancies during prenatal care must be educated of their increased risk, even if they do not exhibit antenatal depressive symptoms. This study aimed to examine the effect of unplanned pregnancy on antenatal depression. Subjects and Method: This was a meta-analysis and systematic review toward unplanned pregnancy and antenatal depression. The study was conducted by selected published articles from 2010 to 2020 in Google Scholar, PubMed, and Springer Link electronic databases. “unplanned pregnancy” AND “antenatal depression” OR “risk factor” AND “antenatal depression” OR “antenatal depression” AND “cross sectional” AND “EPDS” AND “adjusted odd ratio” keywords were used to collected the articles. The inclusion criteria were full text, using cross-sectional study, and reporting adjusted odd ratio. The articles were analyzed using PRISMA guidelines and Revman 5.3. Results: 7 articles were reported that unplanned pregnancy escalated the risk of antenatal depression (aOR= 2.32; 95% CI=1.86 to 2.90; p<0.001). Conclusion: Unplanned pregnancy escalates the risk of antenatal depression. Keywords: unplanned pregnancy, depression, pregnancy Correspondence: Dinda Anindita Salsabilla. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: dindaaninditasalsa@gmail.com. Mobile: 081249007525. DOI: https://doi.org/10.26911/the7thicph.03.116
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Shannon, Tan Tze Ern, Dai Jingwen Annie, and See Swee Lan. "Speech analysis and depression." In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2016. http://dx.doi.org/10.1109/apsipa.2016.7820752.

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Chen, Chunze. "Analysis on Postpartum Depression." In 2021 4th International Conference on Humanities Education and Social Sciences (ICHESS 2021). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.211220.237.

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Sharma, Bhuvan, Harshita Puri, and Deepika Rawat. "Digital Psychiatry - Curbing Depression using Therapy Chatbot and Depression Analysis." In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018. http://dx.doi.org/10.1109/icicct.2018.8472986.

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Joshi, Jyoti. "An Automated Framework for Depression Analysis." In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2013. http://dx.doi.org/10.1109/acii.2013.110.

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Mantri, Shamla, Pankaj Agrawal, S. S. Dorle, Dipti Patil, and V. M. Wadhai. "Clinical Depression Analysis Using Speech Features." In 2013 6th International Conference on Emerging Trends in Engineering and Technology (ICETET). IEEE, 2013. http://dx.doi.org/10.1109/icetet.2013.32.

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Alghowinem, Sharifa, Roland Goecke, Michael Wagner, Gordon Parker, and Michael Breakspear. "Eye movement analysis for depression detection." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738869.

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Huang, Wei. "Analysis on Depression Among LGBTQ Population." In 2021 2nd International Conference on Mental Health and Humanities Education（ICMHHE 2021）. Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210617.082.

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Звіти організацій з теми "Depression analysis"

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LI, Peng, and Junjun Liu. Effect of statin therapy on moderate-to-severe depression: an updated systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0016.

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Review question / Objective: We aim to assess the antidepressant effects of statin therapy among patients complicated with moderate to severe depression. Condition being studied: Depression is one of the major causes of disability worldwide, and major depressive disorders (MDD) contribute to a significant heavy disease burden, which is expected to be second by 2050, only to heart disease. Despite great improvement in therapy, the treatment efficacy remains low. Therefore, alternative therapies have been intensely investigated. A substantial body of researches have suggested that inflammation is one of the operative pathways between MDD and increased risk of somatic comorbidities, and some specific depressive symptoms. Depression occurs in most patients with cardiac and cerebrovascular disease due to the long-term effects, and depression increases the risk of cardiovascular disease in the population as a whole and in patients with coronary artery disease or stroke. Several observational studies have demonstrated reduced rates of depression among patients taking statins, which may be related to its anti-inflammatory effect. However, whether statin improves the depressive symptoms and its associated mechanism is still mixed. Furthermore, there is little evidence about statin treatment effect in those with moderate to severe depression. In addition, whether the effect of statin treatment on depressive symptom changes with time or is affected by baseline depression severity or percentage change of lipid levels has not been explored in previous studies.
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Zhou, Zhuo, Guixing Xu, Liuyang Huang, Hao Tian, Fengyuan Huang, Yilin Liu, Mingsheng Sun, and Fanrong Liang. Effectiveness and Safety of Electroacupuncture for Depression: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0068.

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Review question / Objective: Is electroacupuncture a safe therapy for the treatment of depression? Is electroacupuncture effective for the treatment of depression, as compared with sham control, or conventional drugs？ Condition being studied: Depression is a mood disorder that causes sufferers to feel sadness, decreased interest, guilt, self-blame, loss of energy, and experience sleep disorders such as insomnia. People suffering from depression even feel they have no way out and have suicidal thoughts. In the United States, the prevalence of a major depressive disorder is 16.2%1-3. The 2010 Global Burden of Disease Study identified major depression as the second leading cause of disability worldwide and a leading cause of the burden of suicide and ischaemic heart disease. At present, depression patients are mainly treated with antidepressants, but the efficacy is extremely unstable. Studies have shown that acupuncture can help improve symptoms in patients with depression, but these clinical studies have not been systematically evaluated, and further confirmation is needed to confirm the efficacy of electroacupuncture in treating depression.
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Joao Guassi Moreira, Joao Guassi Moreira. Is depression contagious? An analysis using college roommates. Experiment, March 2014. http://dx.doi.org/10.18258/2306.

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Yang, Nating, Hua Gan, Yaping Wang, Qingyu Ma, Xuan Zhou, and Jiaxu Chen. Meta analysis of distribution frequency of TCM syndromes in depression. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2021. http://dx.doi.org/10.37766/inplasy2021.4.0052.

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Liu, Ran, Kun Zhang, Guangwei Cui, Qiuyu Tong, Wen Ma, and Weidong Shen. Acupuncture for post-stroke depression: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2020. http://dx.doi.org/10.37766/inplasy2020.5.0117.

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Fan, Beifang, Jianping Ma, Huimin Zhang, Yuhua Liao, Wanxin Wang, Sheng Zhang, Ciyong Lu, and Lan Guo. Association of FKBP5 gene variants with depression susceptibility: a comprehensive meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2020. http://dx.doi.org/10.37766/inplasy2020.8.0086.

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Bernanke, Ben. Employment, Hours, and Earnings in the Depression: An Analysis of EightManufacturing Industries. Cambridge, MA: National Bureau of Economic Research, June 1985. http://dx.doi.org/10.3386/w1642.

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Irwin, Douglas. Anticipating the Great Depression? Gustav Cassel's Analysis of the Interwar Gold Standard. Cambridge, MA: National Bureau of Economic Research, November 2011. http://dx.doi.org/10.3386/w17597.

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bao, qiangji, Xin ting Wu, Shu-Jun Chen, Jin-Tao Zhang, Kai Zhao Zhang, Yu Guo, Xiao-Feng Du, and Ming-Fei Yang. Potential Biomarkers for Post-Stroke Depression: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2022. http://dx.doi.org/10.37766/inplasy2022.4.0142.

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Zhou, Dongdong. Non-pharmacological interventions in relapse prevention of unipolar depression: a network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2020. http://dx.doi.org/10.37766/inplasy2020.4.0072.

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