AIMC Topic: Depressive Disorder, Major
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Effect of Cumulative Exposure on the Efficacy of Paroxetine: A Population Pharmacokinetic-Pharmacodynamic and Machine Learning Analyses.
CPT: pharmacometrics & systems pharmacology
Jun 1, 2025
Selective serotonin reuptake inhibitors (SSRIs) are widely used in depression treatment. However, the relationship between treatment efficacy and plasma concentrations remains unclear. We assessed whether the anti-depressive response can be predicted...
Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression.
Journal of anxiety disorders
Jun 1, 2025
Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders, which typically are assessed quantitatively by rating scales such as PHQ-9 and GAD-7. However, recent advances in natural language processing (NLP) ...
Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.
Computers in biology and medicine
Jun 1, 2025
The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting in inconsistent and imprecise evaluations. To address this issue and facilitate early intervention, t...
Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression.
Medical care
Jun 1, 2025
OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns.
A novel artificial intelligence-based methodology to predict non-specific response to treatment.
Psychiatry research
Jun 1, 2025
Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized clinical trials in major depressive disorder (MDD). The objective of this study is to develop artificial neural network (ANN) models to predict the indi...
Comparative Efficacy of MultiModal AI Methods in Screening for Major Depressive Disorder: Machine Learning Model Development Predictive Pilot Study.
JMIR formative research
May 30, 2025
BACKGROUND: Conventional approaches for major depressive disorder (MDD) screening rely on two effective but subjective paradigms: self-rated scales and clinical interviews. Artificial intelligence (AI) can potentially contribute to psychiatry, especi...
Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets.
Neuroinformatics
May 19, 2025
Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convo...
Major depressive disorder recognition based on electronic handwriting recorded in psychological tasks.
BMC medicine
May 13, 2025
BACKGROUND: This study aimed to determine whether handwriting patterns are altered in individuals experiencing depressive episodes. Additionally, we developed a model for the recognition of major depressive disorder (MDD) based on electronic handwrit...
EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.
Schizophrenia bulletin
May 8, 2025
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG rec...
NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.
Radiology. Artificial intelligence
Mar 1, 2025
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computation...
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