ArticleFrontiers in pharmacology2019
Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 53 citations in OpenAlex.
- Real-world clinical validation study: individualized warfarin dosing strategy integrating genetic polymorphisms and Bayesian.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.European journal of clinical pharmacology · 2026Review
- AI-WAR: a novel warfarin management software with a bidirectional LSTM dosing model improves time in therapeutic range.Frontiers in pharmacology · 2026Article
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 2025Article
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- Machine Learning for Warfarin Therapy: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset.PeerJ. Computer science · 2025Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.Pharmaceutics · 2024Review
- Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.Scientific reports · 2024Article
- Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence.Annals of general psychiatry · 2024Article
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
- Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.Scientific reports · 2023Article
- Artificial intelligence in the field of pharmacy practice: A literature review.Exploratory research in clinical and social pharmacy · 2023Review
- Improved stacking ensemble learning based on feature selection to accurately predict warfarin dose.Frontiers in cardiovascular medicine · 2023Article
- Building and analyzing machine learning-based warfarin dose prediction models using scikit-learn.Translational and clinical pharmacology · 2022Article
- Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies.Journal of personalized medicine · 2022Article
- High alert drugs screening using gradient boosting classifier.Scientific reports · 2021Article
- Development of a system to support warfarin dose decisions using deep neural networks.Scientific reports · 2021Article
- Genetic Ancestry Inference and Its Application for the Genetic Mapping of Human Diseases.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with "normal" (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, "sensitive"), and high (i.e., ≥49 mg/week, "resistant") dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with "normal" dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the "sensitive" group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with "normal," "sensitive," and "resistant" to warfarin were obtained when compared to other populations and previous statistical models.
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