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EEG-derived brainwave patterns for depression diagnosis via hybrid machine learning and deep learning frameworks.
Appl Neuropsychol Adult
January 2025
Faculty Xavier Institute of Engineering, Mahim, India.
In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040.
View Article and Find Full Text PDFTesting the Feasibility, Acceptability, and Potential Efficacy of an Innovative Digital Mental Health Care Delivery Model Designed to Increase Access to Care: Open Trial of the Digital Clinic.
JMIR Ment Health
January 2025
Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
Background: Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care.
View Article and Find Full Text PDFHarnessing Human-Centered Design for Evidence-Based Psychosocial Interventions and Implementation Strategies in Community Settings: Protocol for Redesign to Improve Usability, Engagement, and Appropriateness.
JMIR Res Protoc
January 2025
Division of Services and Interventions Research, National Institute of Mental Health, Bethesda, MD, United States.
Background: Although substantial progress has been made in establishing evidence-based psychosocial clinical interventions and implementation strategies for mental health, translating research into practice-particularly in more accessible, community settings-has been slow.
Objective: This protocol outlines the renewal of the National Institute of Mental Health-funded University of Washington Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness Center, which draws from human-centered design (HCD) and implementation science to improve clinical interventions and implementation strategies. The Center's second round of funding (2023-2028) focuses on using the Discover, Design and Build, and Test (DDBT) framework to address 3 priority clinical intervention and implementation strategy mechanisms (ie, usability, engagement, and appropriateness), which we identified as challenges to implementation and scalability during the first iteration of the center.
Association of Year-to-Year Lipid Variability With Risk of Cognitive Decline and Dementia in Community-Dwelling Older Adults.
Neurology
February 2025
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Background And Objectives: Lipid metabolism in older adults is affected by various factors including biological aging, functional decline, reduced physiologic reserve, and nutrient intake. The dysregulation of lipid metabolism could adversely affect brain health. This study investigated the association between year-to-year intraindividual lipid variability and subsequent risk of cognitive decline and dementia in community-dwelling older adults.
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