AIMC Topic: Antidepressive Agents
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Cross-trial prediction of treatment outcome in depression: a machine learning approach.
The lancet. Psychiatry
Jan 21, 2016
BACKGROUND: Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific ...
Antidepressant use in late gestation and risk of postpartum haemorrhage: a retrospective cohort study.
BJOG : an international journal of obstetrics and gynaecology
Sep 15, 2015
OBJECTIVE: To investigate the association between antidepressant use in late gestation and postpartum haemorrhage (PPH).
Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder.
Clinical and translational science
Jun 1, 2025
Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary...
Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.
Psychiatry research
Jun 1, 2025
The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose significant concerns. This study investigates potential biases in chatbot-generated recommendations ...
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...
Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.
Clinical and translational science
Jan 1, 2025
Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to develop antidepressant medications appropriate for outperforming placebo. Treatment effect in randomized,...
Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.
Human brain mapping
Jan 1, 2025
Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that pr...
Biologically plausible models of neural dynamics for rapid-acting antidepressant interventions.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Jan 1, 2021
Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery.
The international journal of neuropsychopharmacology
Nov 26, 2020
BACKGROUND: There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of...
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