AIMC Topic: Antidepressive Agents
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Optimizing the Prediction of Depression Remission: A Longitudinal Machine Learning Approach.
American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics
Oct 29, 2024
Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. P...
Identifying the most critical side effects of antidepressant drugs: a new model proposal with quantum spherical fuzzy M-SWARA and DEMATEL techniques.
BMC medical informatics and decision making
Sep 28, 2024
Identifying and managing the most critical side effects encourages patients to take medications regularly and adhere to the course of treatment. Therefore, priority should be given to the more important ones, among these side effects. However, the nu...
Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Sep 17, 2024
OBJECTIVE: Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroe...
Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.
Clinical EEG and neuroscience
Sep 9, 2024
Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Id...
Machine learning for antidepressant treatment selection in depression.
Drug discovery today
Jun 24, 2024
Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In th...
Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.
Translational psychiatry
Jun 21, 2024
Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to u...
At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data.
Journal of affective disorders
May 27, 2024
BACKGROUND: Improving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal.
Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model.
Journal of hazardous materials
May 22, 2024
As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occur...
Optimizing precision medicine for second-step depression treatment: a machine learning approach.
Psychological medicine
Mar 27, 2024
BACKGROUND: Less than a third of patients with depression achieve successful remission with standard first-step antidepressant monotherapy. The process for determining appropriate second-step care is often based on clinical intuition and involves a p...
Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing.
Psychiatry research
Feb 25, 2024
Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using s...
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