Depression Assessment

2021 ◽  
Author(s):  
Carla D. Edwards
Keyword(s):  
Depression Assessment

A preliminary fuzzy cognitive map - based desicion support tool for geriatric depression assessment

2013 ◽  
Author(s):  
Elpiniki I. Papageorgiou ◽  
Antonis S. Billis ◽  
Christos Frantzidis ◽  
Evdokimos I. Konstantinidis ◽  
Panagiotis D. Bamidis
Keyword(s):  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Geriatric Depression ◽  
Support Tool ◽  
Depression Assessment

Clinical and psychometric validation of the psychotic depression assessment scale

2015 ◽  
Vol 173 ◽  
pp. 261-268 ◽  
Author(s):  
Søren D. Østergaard ◽  
Christina H. Pedersen ◽  
Peter Uggerby ◽  
Povl Munk-Jørgensen ◽  
Anthony J. Rothschild ◽  
...  
Keyword(s):  
Assessment Scale ◽  
Psychometric Validation ◽  
Psychotic Depression ◽  
Depression Assessment

scholarly journals Depression-level assessment from multi-lingual conversational speech data using acoustic and text features

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Cenk Demiroglu ◽  
Aslı Beşirli ◽  
Yasin Ozkanca ◽  
Selime Çelik
Keyword(s):  
Text Analysis ◽  
Training Data ◽  
Conversational Speech ◽  
Acoustic Feature ◽  
The World ◽  
Significant Burden ◽  
Depression Assessment ◽  
Depression Level ◽  
Text Features ◽  
Multiple Languages

AbstractDepression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.

scholarly journals Prevalence of pre-stroke depression and its association with post-stroke depression: a systematic review and meta-analysis

2018 ◽  
Vol 49 (4) ◽  
pp. 685-696 ◽  
Author(s):  
Martin Taylor-Rowan ◽  
Oyiza Momoh ◽  
Luis Ayerbe ◽  
Jonathan J. Evans ◽  
David J. Stott ◽  
...  
Keyword(s):  
Systematic Review ◽  
Publication Bias ◽  
Meta Analysis ◽  
Assessment Method ◽  
Self Report ◽  
Funnel Plot ◽  
Strength Of Evidence ◽  
Post Stroke ◽  
Depression Assessment ◽  
Post Stroke Depression

AbstractBackgroundDepression is a common post-stroke complication. Pre-stroke depression may be an important contributor, however the epidemiology of pre-stroke depression is poorly understood. Using systematic review and meta-analysis, we described the prevalence of pre-stroke depression and its association with post-stroke depression.MethodsWe searched multiple cross-disciplinary databases from inception to July 2017 and extracted data on the prevalence of pre-stroke depression and its association with post-stroke depression. We assessed the risk of bias (RoB) using validated tools. We described summary estimates of prevalence and summary odds ratio (OR) for association with post-stroke depression, using random-effects models. We performed subgroup analysis describing the effect of depression assessment method. We used a funnel plot to describe potential publication bias. The strength of evidence presented in this review was summarised via ‘GRADE’.ResultsOf 11 884 studies identified, 29 were included (total participantsn= 164 993). Pre-stroke depression pooled prevalence was 11.6% [95% confidence interval (CI) 9.2–14.7]; range: 0.4–24% (I295.8). Prevalence of pre-stroke depression varied by assessment method (p= 0.02) with clinical interview suggesting greater pre-stroke depression prevalence (~17%) than case-note review (9%) or self-report (11%). Pre-stroke depression was associated with increased odds of post-stroke depression; summary OR 3.0 (95% CI 2.3–4.0). All studies were judged to be at RoB: 59% of included studies had an uncertain RoB in stroke assessment; 83% had high or uncertain RoB for pre-stroke depression assessment. Funnel plot indicated no risk of publication bias. The strength of evidence based on GRADE was ‘very low’.ConclusionsOne in six stroke patients have had pre-stroke depression. Reported rates may be routinely underestimated due to limitations around assessment. Pre-stroke depression significantly increases odds of post-stroke depression.Protocol identifierPROSPERO identifier: CRD42017065544

scholarly journals Robustness and Findings of a Web-Based System for Depression Assessment in a University Work Context

2019 ◽  
Vol 16 (4) ◽  
pp. 644 ◽  
Author(s):  
Sabina Asensio-Cuesta ◽  
Adrián Bresó ◽  
Carlos Saez ◽  
Juan García-Gómez
Keyword(s):  
Internal Consistency Reliability ◽  
Work Context ◽  
Web Based ◽  
Staff Members ◽  
Significant Difference ◽  
Depression Assessment ◽  
Working Community ◽  
High Level ◽  
The University ◽  
Web Based System

Depression is associated with absenteeism and presentism, problems in workplace relationships and loss of productivity and quality. The present work describes the validation of a web-based system for the assessment of depression in the university work context. The basis of the system is the Spanish version of the Beck Depression Inventory (BDI-II). A total of 185 participants completed the BDI-II web-based assessment, including 88 males and 97 females, 70 faculty members and 115 staff members. A high level of internal consistency reliability was confirmed. Based on the results of our web-based BDI-II, no significant differences were found in depression severity between gender, age or workers’ groups. The main depression risk factors reported were: “Changes in sleep”, “Loss of energy”, “Tiredness or fatigue” and “Loss of interest”. However significant differences were found by gender in “Changes in appetite”, “Difficulty of concentration” and “Loss of interest in sex”; males expressed less loss of interest in sex than females with a statistically significant difference. Our results indicate that the data collected is coherent with previous BDI-II studies. We conclude that the web-based system based on the BDI-II is psychometrically robust and can be used to assess depression in the university working community.

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