Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients
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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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All three studies comparing logistic regression to machine learning algorithms for predicting stable tacrolimus dose in kidney transplant patients showed a superior predictive ability of machine learning tools over linear regression models, albeit with a relatively small difference.
“All studies showed a superior predictive ability of machine learning tools over the linear regression models, albeit with a relatively small difference.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [33]
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- Authors
- Tang, J., Liu, R., Zhang, Y.-L., Liu, M.-Z., Hu, Y.-F., Shao, M.-J., Zhu, L.-J., Xin, H.-W., Feng, G.-W., Shang, W.-J., Meng, X.-G., Zhang, L.-R., Ming, Y.-Z., & Zhang, W.
- Affiliations
- Central South University (CN)
- Wuhan General Hospital of Guangzhou (CN)
- Zhengzhou University (CN)
- Corresponding author
- Zhang, Wei
- Venue
- Scientific Reports
- Published
- 2017-02-08
- Volume
- 7
- Issue
- 1
- Pages
- 42192-42192
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5296901
- OpenAlex
- W2587106090
- Cited by
- 154
- References
- 36
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 24 · 2023: 22 · 2022: 21 · 2021: 22 · 2020: 22 · 2019: 6 · 2018: 4 · 2017: 3
- FWCI
- 8.32
- Type
- journal article
- Language
- en
- Update notices
- correction (2018-01-29)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Key Research and Development Program of China (2016YFC0905000, 2016YFC0905001, No 2016YFC0905000)
- Topics
- Health Sciences › Medicine › Transplantation › Renal Transplantation Outcomes and Treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Keywords
- tacrolimus, kidney transplant recipients, pharmacogenetics, machine learning, regression trees, multiple linear regression, personalized medicine, immunosuppressive dosing, Chinese patients
- MeSH terms
- Machine Learning, Adult, Bayes Theorem, Female, Humans, Immunosuppressive Agents, Male, Middle Aged, Cohort Studies, Multivariate Analysis, Linear Models, Kidney Transplantation, Tacrolimus, Renal Insufficiency, Chronic, Drug Dosage Calculations, Precision Medicine, Transplant Recipients
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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