A5053677506- Mental Health Research Topics
- Digital Mental Health Interventions
- Treatment of Major Depression
- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare and Education
- Mental Health Treatment and Access
- Functional Brain Connectivity Studies
- Health Systems, Economic Evaluations, Quality of Life
- Tryptophan and brain disorders
- Suicide and Self-Harm Studies
- Computational Drug Discovery Methods
- Patient-Provider Communication in Healthcare
- Ethics and Social Impacts of AI
- Systemic Lupus Erythematosus Research
- Neural Networks and Applications
- Psychosomatic Disorders and Their Treatments
- Multiple Sclerosis Research Studies
- Adversarial Robustness in Machine Learning
- Advanced Computational Techniques and Applications
- Mental Health via Writing
- Telemedicine and Telehealth Implementation
- Advanced Data Processing Techniques
- Herpesvirus Infections and Treatments
RELX Group (Netherlands)
2024
University of Arizona
2024
Alfred Health
2022
Montreal Neurological Institute and Hospital
2018-2019
McGill University
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...
Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep models clinically or etiologically meaningful way. In this paper, we describe methods for analyzing clinical and demographic psychiatric data, using our recent work on model STAR*D CO-MED remission prediction. Methods: Our analysis yielded four that predicted the treatments...
Background Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians treatment selection and management, improving personalization best practices such as measurement-based care. Previous literature shows that for digital mental health tools be successful, tool must easy feasible...
Depression affects one in nine people, but treatment response rates remain low. There is significant potential the use of computational modeling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning a promising technique that can be used for differential selection based on predicted remission probability. Using Sequenced Treatment Alternatives Relieve (STAR*D) Combining Medications Enhance Outcomes (CO-MED) trial data, we employed deep...
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...
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of data, images are typically normalized to a reference. The most convenient reference tissue that always present in the image, and unlikely be affected by pathological processes. multiple sclerosis neuroimaging, both white gray matter affected, so normalization techniques depend on brain may introduce...
The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship a novel, artificial-intelligence (AI) enabled decision support system (CDSS) for use in treating adults with major depression. A single arm, naturalistic follow-up study aimed at assessing the acceptability useability software. Patients had baseline appointment, followed by minimum two appointments CDSS. Study exit questionnaires interviews were conducted assess utility,...
Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering patients; or sub-grouping personal differences within the identified groups. While both paradigms have shown promising results, each them suffers from important limitations. In this article, we propose novel deep learning-based approach that is to strike balance between using latent-space prototyping. Our specifically tailored for domains in...
Abstract Background Depression affects one in nine people, but treatment response rates remain low. There is significant potential the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning a promising technique that can be used for differential selection based on predicted remission probability. Methods Using STAR*D CO-MED trial data, we employed deep neural networks after feature selection. Differential...
Mental health conditions cause a great deal of distress or impairment; depression alone will affect 11% the world's population. The application Artificial Intelligence (AI) and big-data technologies to mental has potential for personalizing treatment selection, prognosticating, monitoring relapse, detecting helping prevent before they reach clinical-level symptomatology, even delivering some treatments. However, unlike similar applications in other fields medicine, there are several unique...
<title>Abstract</title> We introduce an artificial intelligence (AI) model aiming to personalize treatment in adult major depression, which was deployed the Artificial Intelligence Depression: Medication Enhancement (AID-ME) Study. Our objectives were predict probabilities of remission across multiple pharmacological treatments, validate predictions, and examine them for biases. Data from 9,042 adults with moderate severe depression antidepressant clinical trials standardized into a common...
Introduction: Suicidal ideation (SI) is prevalent in the general population, and a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be useful tool this context, as it can used find patterns complex, heterogeneous, incomplete datasets. An automated screening system could help prompt clinicians more attentive at Methods: Using Canadian Community Health Survey—Mental Component, we trained DL model based on 23,859 survey...
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INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize and improve outcomes, which was deployed in the Artificial Intelligence Depression Medication Enhancement (AIDME) Study. OBJECTIVES: 1) Develop capable predicting probabilities remission across multiple treatments for adults with at least moderate major depression. 2) Validate predictions examine them...
Abstract Major Depressive Disorder (MDD) is a leading cause of disability and there paucity tools to personalize manage treatments. A cluster-randomized, patient-and-rater-blinded, clinician-partially-blinded study was conducted assess the effectiveness safety Aifred Clinical Decision Support System (CDSS) facilitating algorithm-guided care predicting medication remission probabilities using clinical data. Clinicians were randomized Active (CDSS access) or Active-Control group...