ArticlePloS one2018
Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Pharmacogenomics of antibacterial and antiviral therapies: Clinical actionability, evidence gaps, and future directions.Pharmacological reviews · 2026Review
- Advancing the development of deep learning and machine learning models for oral drugs through diverse descriptor classes: a focus on pharmacokinetic parameters (Vdss and PPB).Molecular diversity · 2026Article
- Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.European journal of clinical pharmacology · 2026Review
- Machine Learning for Warfarin Therapy: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 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
- The Role of Vitamin D Metabolism Genes and Their Genomic Background in Shaping Cyclosporine A Dosage Parameters after Kidney Transplantation.Journal of clinical medicine · 2024Article
- Leveraging AI techniques for predicting spatial distribution and determinants of carbon emission in China's Yangtze River Delta.Scientific reports · 2024Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 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
- Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning.Frontiers in pharmacology · 2024Article
- A Real-World Exploration into Clinical Outcomes of Direct Oral Anticoagulant Dosing Regimens in Morbidly Obese Patients Using Data-Driven Approaches.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2023Observational
- Evaluation of supervised machine learning algorithms in predicting the poor anticoagulation control and stable weekly doses of warfarin.International journal of clinical pharmacy · 2023Article
- 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
- Distributed information encoding and decoding using self-organized spatial patterns.Patterns (New York, N.Y.) · 2022Article
- Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach.Interactive journal of medical research · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The most widely validated pharmacogenetic algorithm is the IWPC algorithm based on multivariate linear regression (MLR). However, with only a single algorithm, the prediction performance may reach an upper limit even with optimal parameters. Here, we present novel algorithms using stacked generalization frameworks to estimate the warfarin dose, within which different types of machine learning algorithms function together through a meta-machine learning model to maximize the prediction accuracy. Compared to the IWPC-derived MLR algorithm, Stack 1 and 2 based on stacked generalization frameworks performed significantly better overall. Subgroup analysis revealed that the mean of the percentage of patients whose predicted dose of warfarin within 20% of the actual stable therapeutic dose (mean percentage within 20%) for Stack 1 was improved by 12.7% (from 42.47% to 47.86%) in Asians and by 13.5% (from 22.08% to 25.05%) in the low-dose group compared to that for MLR, respectively. These data suggest that our algorithms would especially benefit patients requiring low warfarin maintenance dose, as subtle changes in warfarin dose could lead to adverse clinical events (thrombosis or bleeding) in patients with low dose. Our study offers novel pharmacogenetic algorithms for clinical trials and practice.
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