ArticleScientific reports2017
Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 70 papers, 1 of them a synthesis that pooled it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence.Frontiers in pharmacology · 2020Pooled it
- Narrow Therapeutic Index Drugs: Regulatory Frameworks, Bioequivalence, and Model-Informed Precision Dosing.Pharmaceutics · 2026Review
- Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.Mayo Clinic proceedings. Digital health · 2026Review
- Mechanism-informed machine learning for individualized tacrolimus dose adjustment in the early post-kidney transplant period.British journal of clinical pharmacology · 2026Article
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.Frontiers in immunology · 2026Review
- Third annual transplant AI symposium: from organ matching to digital twins.Frontiers in transplantation · 2026Article
- Explainable machine learning framework using visceral adiposity index to predict cardiorenal syndrome: a survey-weighted NHANES study with SHAP interpretation.Renal failure · 2025Article
- Article
- Integration of urine retinol-binding protein and genetic markers for early prediction of tacrolimus nephrotoxicity using machine learning.Translational pediatrics · 2025Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Comparison of Machine Learning Algorithms and Bayesian Estimation in Predicting Tacrolimus Concentration in Tunisian Kidney Transplant Patients During the Early Post-Transplant Period.European journal of drug metabolism and pharmacokinetics · 2025Article
- Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.CPT: pharmacometrics & systems pharmacology · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest.Frontiers in pharmacology · 2025Article
- State of Art of Dose Individualization to Support tacrolimus drug monitoring: What's Next?Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
- Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Article
- Analytical modelling techniques for enhancing tacrolimus dosing in solid organ transplantation: a systematic review protocol.BMJ open · 2024Article
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching.Diagnostics (Basel, Switzerland) · 2024Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the "derivation cohort" to develop dose-prediction algorithm, while the remaining 20% constituted the "validation cohort" to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.
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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.