ArticleInternational journal of clinical pharmacy2024
Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence.
Article in International journal of clinical pharmacy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.International journal of clinical pharmacy · 2026Article
- Influence ofPharmaceuticals (Basel, Switzerland) · 2026Article
- An Integrated Population Pharmacokinetic and Machine Learning Model for Predicting Tacrolimus Exposure in Adult Patients with Nephrotic Syndrome.Drug design, development and therapy · 2026Article
- Development of a machine learning-based risk prediction model and analysis of risk factors for docetaxel-induced bone marrow suppression in breast cancer patients.International journal of clinical pharmacy · 2025Article
- Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.International journal of clinical pharmacy · 2025Article
- Machine learning-based prediction model for teicoplanin plasma concentrations in adults with liver disease using real-world data.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundVenlafaxine 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.
aimOur objective was to develop a prediction model for venlafaxine concentration using real-world evidence based on machine learning and deep learning techniques.
methodPatients who underwent venlafaxine treatment between November 2019 and August 2022 were included in the study. Important variables affecting venlafaxine concentration were identified using a combination of univariate analysis, sequential forward selection, and machine learning techniques. Predictive performance of nine machine learning and deep learning algorithms were assessed, and the one with the optimal performance was selected for modeling. The final model was interpreted using SHapley Additive exPlanations.
resultsA total of 330 eligible patients were included. Five influential variables that affect venlafaxine concentration were venlafaxine daily dose, sex, age, hyperlipidemia, and adenosine deaminase. The venlafaxine concentration prediction model was developed using the eXtreme Gradient Boosting algorithm (R
conclusionThe XGBoost model for predicting blood concentration of venlafaxine using real-world evidence was developed, guiding the adjustment of regimen in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.