ArticlePharmaceutics2022
Application of Machine Learning Classification to Improve the Performance of Vancomycin Therapeutic Drug Monitoring.
Article in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
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
14 citing papers in PubMed, 20 citations in OpenAlex.
- Development and evaluation of machine learning-based prediction-modelling for initial vancomycin serum concentrations in septic ICU patients using clinical health record data.Intensive care medicine experimental · 2026Article
- Machine Learning Modeling for Predicting Infliximab Pharmacokinetics in Pediatric and Young Adult Patients With Crohn Disease: Leveraging Ensemble Modeling With Synthetic and Real-World Data.Therapeutic drug monitoring · 2026Article
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- Population Pharmacokinetic Model Evaluation with a Small Real-World Dataset Versus a Large Virtual Dataset: Does Sample Size Affect Decision-Making?European journal of drug metabolism and pharmacokinetics · 2025Article
- Machine Learning Prediction and Validation of Plasma Concentration-Time Profiles.Molecular pharmaceutics · 2025Article
- Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.Pharmaceutical research · 2025Article
- Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.Archives of toxicology · 2024Article
- A Deep Learning-Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness.JMIR formative research · 2024Article
- Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring.Frontiers in pharmacology · 2024Article
- Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model.CPT: pharmacometrics & systems pharmacology · 2023Article
- Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients.BMC medical informatics and decision making · 2022Article
- Use of Antibiotics in Preterm Newborns.Antibiotics (Basel, Switzerland) · 2022Review
- An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine.Frontiers in pharmacology · 2022Article
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Bayesian therapeutic drug monitoring (TDM) software uses a reported pharmacokinetic (PK) model as prior information. Since its estimation is based on the Bayesian method, the estimation performance of TDM software can be improved using a PK model with characteristics similar to those of a patient. Therefore, we aimed to develop a classifier using machine learning (ML) to select a more suitable vancomycin PK model for TDM in a patient. In our study, nine vancomycin PK studies were selected, and a classifier was created to choose suitable models among them for patients. The classifier was trained using 900,000 virtual patients, and its performance was evaluated using 9000 and 4000 virtual patients for internal and external validation, respectively. The accuracy of the classifier ranged from 20.8% to 71.6% in the simulation scenarios. TDM using the ML classifier showed stable results compared with that using single models without the ML classifier. Based on these results, we have discussed further development of TDM using ML. In conclusion, we developed and evaluated a new method for selecting a PK model for TDM using ML. With more information, such as on additional PK model reporting and ML model improvement, this method can be further enhanced.
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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.