Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Late life depression(LLD)"'
Autor:
Fugui Jiang, Jing Zhang, Yang Yi, Arui Tan, Xiaohong Qin, Peijia Wang, Xuemei Zhong, Jun Xiao, Jieying Li, Bo Zhou
Publikováno v:
Comprehensive Psychiatry, Vol 133, Iss , Pp 152490- (2024)
Background: Late-Life Depression (LLD) is a prevalent mental health disorder that is often accompanied by cognitive impairments. The objective of this study is to investigate the influence of coexisting Generalized Anxiety Disorder (GAD) on both subj
Externí odkaz:
https://doaj.org/article/40bfdb39b3f042a89f2a5e3159522ae8
Autor:
Yuanzhi Zhao, Xiangping Wu, Min Tang, Lingli Shi, Shuang Gong, Xi Mei, Zheng Zhao, Jiayue He, Ling Huang, Wei Cui
Publikováno v:
Frontiers in Psychiatry, Vol 14 (2023)
Late-life depression (LLD) is one of the most common mental disorders among the older adults. Population aging, social stress, and the COVID-19 pandemic have significantly affected the emotional health of older adults, resulting in a worldwide preval
Externí odkaz:
https://doaj.org/article/3b4895f66e8a4a129870ab7e242023f8
Autor:
Weijian Liu, Hui Li, Xiao Lin, Peng Li, Ximei Zhu, Sizhen Su, Jie Shi, Lin Lu, Jiahui Deng, Xinyu Sun
Publikováno v:
Frontiers in Aging Neuroscience, Vol 14 (2022)
Facial emotion recognition plays an important role in social functioning. Patients with late-life depression (LLD) often have abnormal facial emotion recognition. Mindfulness-based cognitive therapy (MBCT) is beneficial in treating depression. This s
Externí odkaz:
https://doaj.org/article/7df52f8448e24f8290d89aa1e395872a
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Marieke J. Henstra, Thomas C. Feenstra, Rob M. Kok, Harm-Pieter Spaans, Eric van Exel, Annemiek Dols, Mardien Oudega, Anton C. M. Vergouwen, Adriano van der Loo, Pierre M. Bet, Stephan A. Loer, Merijn Eikelenboom, Pascal Sienaert, Simon Lambrichts, Filip Bouckaert, Judith E. Bosmans, Nathalie van der Velde, Aartjan T. F. Beekman, Max L. Stek, Didi Rhebergen
Publikováno v:
Frontiers in Psychiatry, Vol 13 (2022)
BackgroundCognitive side-effects are an important reason for the limited use of electroconvulsive therapy (ECT). Cognitive side-effects are heterogeneous and occur frequently in older persons. To date, insight into these side-effects is hampered due
Externí odkaz:
https://doaj.org/article/05849dcdd85d453c8fbb5959a0a507e3
Autor:
Adrienne Grzenda, William Speier, Prabha Siddarth, Anurag Pant, Beatrix Krause-Sorio, Katherine Narr, Helen Lavretsky
Publikováno v:
Frontiers in Psychiatry, Vol 12 (2021)
Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using d
Externí odkaz:
https://doaj.org/article/ce476e2079554dc1872dd809d456e66c
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Journal of Geriatric Mental Health, Vol 2, Iss 2, Pp 96-101 (2015)
Introduction: Despite a plethora of prevalence studies, research on the course and outcome of late-life depression (LLD) have received little attention in India. Thus, the present study was undertaken to explore the course and outcome of LLD. Materia
Externí odkaz:
https://doaj.org/article/ccc858051f514efca5370e1fbc3a891c
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Prabha Siddarth, Beatrix Krause-Sorio, Adrienne Grzenda, William Speier, Anurag Pant, Helen Lavretsky, Katherine L. Narr
Publikováno v:
Frontiers in Psychiatry
Frontiers in Psychiatry, Vol 12 (2021)
Frontiers in Psychiatry, Vol 12 (2021)
Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebe2fd8c69eaef0c089e7c9d61581644
https://doi.org/10.3389/fpsyt.2021.738494
https://doi.org/10.3389/fpsyt.2021.738494