AIMC Topic: Venlafaxine Hydrochloride

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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 Retrospective Studies Female Aged Adult Male Middle Aged Depression Antidepressive Agents, Second-Generation Venlafaxine Hydrochloride
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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.
Humans Artificial Intelligence Precision Medicine Retrospective Studies Female Aged Adult Male Middle Aged Depression Dose-Response Relationship, Drug Antidepressive Agents, Second-Generation Venlafaxine Hydrochloride
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Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

JAMA network open Jun 1, 2020
IMPORTANCE: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.
Humans Machine Learning Algorithms Prospective Studies Prognosis Female Adult Male Middle Aged Depressive Disorder, Major Treatment Outcome Electroencephalography Antidepressive Agents Citalopram Clinical Decision Rules Sertraline Venlafaxine Hydrochloride
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