A5077185046- Mental Health Research Topics
- Digital Mental Health Interventions
- Treatment of Major Depression
- Mental Health Treatment and Access
- Tryptophan and brain disorders
- Biomedical Text Mining and Ontologies
- Dementia and Cognitive Impairment Research
- Artificial Intelligence in Healthcare and Education
- Machine Learning in Healthcare
McGill University
2018-2024
Montreal Neurological Institute and Hospital
2018
Montreal Children's Hospital
2018
Background Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these impact physician-patient interaction. Aims Aifred an clinical decision support system (CDSS) treatment major depression. Here, we explore use a simulation centre environment in evaluating usability Aifred, particularly its on physician–patient Method Twenty psychiatry and family medicine attending staff...
The Canadian Institutes for Health Research (CIHR) launched the "International Collaborative Strategy Alzheimer's Disease" as a signature initiative, focusing on Disease (AD) and related neurodegenerative disorders (NDDs). Consortium Neurodegeneration Aging (CCNA) was subsequently established to coordinate strengthen research AD NDDs. To facilitate this research, CCNA uses LORIS, modular data management system that integrates acquisition, storage, curation, dissemination across multiple...
Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given varied presentation MDD heterogeneity response, use machine learning to understand complex, non-linear relationships in data may be key for personalization. Well-organized, structured from clinical trials with standardized outcome measures useful training models; however, combining across poses numerous challenges. There also persistent concern...
ABSTRACT Objective Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via “trial and error”. Given varied presentation MDD heterogeneity response, use machine learning to understand complex, non-linear relationships in data may be key for personalization. Well-organized, structured from clinical trials with standardized outcome measures useful training models; however, combining across poses numerous challenges. There also...
ABSTRACT Objective Aifred is an artificial intelligence (AI)-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore use a simulation centre environment in evaluating usability Aifred, particularly its impact on physician-patient interaction. Methods Twenty psychiatry and family medicine attending staff residents were recruited to complete 2.5-hour study at interaction with standardized patients. Each physician had option using CDSS inform...