ArticleFrontiers in medicine2022
A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 36 citations in OpenAlex.
- Narrow Therapeutic Index Drugs: Regulatory Frameworks, Bioequivalence, and Model-Informed Precision Dosing.Pharmaceutics · 2026Review
- Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.International journal of clinical pharmacy · 2026Article
- 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
- Analytical Models to Optimize Tacrolimus Dosing in Solid Organ Transplantation: A Systematic Review.Pharmaceutics · 2026Review
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
- Artificial Intelligence and Predictive Modelling for Precision Dosing of Immunosuppressants in Kidney Transplantation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.Frontiers in immunology · 2026Review
- Towards Personalized Tacrolimus Dosing Using an Algorithm-Driven Prediction Pipeline for Kidney Transplant.Drug design, development and therapy · 2026Article
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
- Article
- Optimizing Tacrolimus Dosing During Hospitalization After Kidney Transplantation: A Comparative Model Analysis.Annals of transplantation · 2025Article
- Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach.Scientific reports · 2025Article
- Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest.Frontiers in pharmacology · 2025Article
- Angiotensin II type 1 receptor antibodies as a contributor to microvascular inflammation in kidney transplant recipients: insights from statistical and artificial intelligence based approaches.Frontiers in immunology · 2025Article
- Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.Frontiers in medicine · 2024Article
- Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients.Frontiers in pharmacology · 2024Article
- Construction and evaluation of glucocorticoid dose prediction model based on genetic and clinical characteristics of patients with systemic lupus erythematosus.International journal of immunopathology and pharmacologyArticle
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R
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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.