AIMC Topic: Antidepressive Agents, Second-Generation

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Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence.

International journal of clinical pharmacy May 16, 2024
BACKGROUND: Venlafaxine is frequently prescribed for patients with depression. To control the concentration of venlafaxine within the therapeutic window for the best treatment effect, a model to predict venlafaxine concentration is necessary.
Humans Machine Learning Aged Adult Male Retrospective Studies Female Antidepressive Agents, Second-Generation Middle Aged Venlafaxine Hydrochloride Depression
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Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data.

International journal of clinical pharmacy May 11, 2024
BACKGROUND: Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing.
Adult Male Female Humans Aged Artificial Intelligence Dose-Response Relationship, Drug Precision Medicine Middle Aged Depression Retrospective Studies Antidepressive Agents, Second-Generation Venlafaxine Hydrochloride
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Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

JAMA network open Jan 3, 2020
IMPORTANCE: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment ...
Citalopram Antidepressive Agents, Second-Generation Prognosis Male Treatment Outcome Depressive Disorder, Major Middle Aged Canada Support Vector Machine Predictive Value of Tests Machine Learning Adult Electroencephalography Humans Biomarkers Reproducibility of Results Sensitivity and Specificity Female
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Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression.

Biological psychiatry. Cognitive neuroscience and neuroimaging Oct 22, 2019
BACKGROUND: Theoretical models have emphasized systems-level abnormalities in major depressive disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven ap...
Double-Blind Method Reward Dopamine Machine Learning Antidepressive Agents, Second-Generation Depressive Disorder, Major Amisulpride Female Depression Young Adult Adult Humans Male
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A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry Jan 11, 2018
OBJECTIVE: Despite efforts to identify characteristics associated with medication-placebo differences in antidepressant trials, few consistent findings have emerged to guide participant selection in drug development settings and differential therapeu...
Citalopram Antidepressive Agents, Second-Generation Aged, 80 and over Single-Blind Method Late Onset Disorders Female Placebo Effect Male Aged Depression Humans Treatment Outcome Machine Learning
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