ArticleComputational and mathematical methods in medicine2015
Revisiting Warfarin Dosing Using Machine Learning Techniques.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integrating artificial intelligence into venous thromboembolism care: Predictive models, implementation challenges, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Pharmacogenomics of antibacterial and antiviral therapies: Clinical actionability, evidence gaps, and future directions.Pharmacological reviews · 2026Review
- Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.European journal of clinical pharmacology · 2026Review
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- In-house chromogenic anti-factor Xa assay: development, validation, and identification of factors predicting APTT discordance.Frontiers in medicine · 2025Article
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
- Article
- Control-Relevant Adaptive Personalized Modeling From Limited Clinical Data for Precise Warfarin Management.IEEE open journal of engineering in medicine and biology · 2022Article
- Machine Learning in Healthcare.Current genomics · 2021Review
- Review
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Development of a system to support warfarin dose decisions using deep neural networks.Scientific reports · 2021Article
- A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters.Scientific reports · 2021Article
- DBCSMOTE: a clustering-based oversampling technique for data-imbalanced warfarin dose prediction.BMC medical genomics · 2020Article
- Article
- The Prediction Model of Warfarin Individual Maintenance Dose for Patients Undergoing Heart Valve Replacement, Based on the Back Propagation Neural Network.Clinical drug investigation · 2020Article
- Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.Frontiers in pharmacology · 2019Article
- Warfarin maintenance dose Prediction for Patients undergoing heart valve replacement- a hybrid model with genetic algorithm and Back-Propagation neural network.Scientific reports · 2018Article
- A New Approach towards Minimizing the Risk of Misdosing Warfarin Initiation Doses.Computational and mathematical methods in medicine · 2018Article
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which additionally contain the genetic variables. In this paper, a new methodology for warfarin dosing is proposed. The patients are initially classified into two classes. The first class contains patients who require doses of >30 mg/wk and the second class contains patients who require doses of ≤30 mg/wk. This phase is performed using relevance vector machines. In the second phase, the optimal dose for each patient is predicted by two clinical regression models that are customized for each class of patients. The prediction accuracy of the model was 11.6 in terms of root mean squared error (RMSE) and 8.4 in terms of mean absolute error (MAE). This was 15% and 5% lower than IWPC and Gage models (which are the most widely used models in practice), respectively, in terms of RMSE. In addition, the proposed model was compared with fixed-dose approach of 35 mg/wk, and the model proposed by Sharabiani et al. and its outperformance were proved in terms of both MAE and RMSE.
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