ArticleMolecular diagnosis & therapy2014
Dosage individualization of warfarin using artificial neural networks.
Article in Molecular diagnosis & therapy, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence.Frontiers in pharmacology · 2020Pooled it
- Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.Scientific reports · 2023Article
- Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies.Journal of personalized medicine · 2022Article
- Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis.Pharmaceutics · 2021Article
- Establishment of a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm.Medicine · 2018Article
- Warfarin Pharmacogenomics in Diverse Populations.Pharmacotherapy · 2017Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesOur objective was to explore artificial neural networks (ANNs) as a possible tool for dosage individualization of warfarin.
methodsDemographic, clinical, and genetic data were gathered from a previously collected cohort of patients with a stable warfarin dosage who were able to achieve an observed international normalized ratio of 2-3. Data from a cohort of 3,415 patients were used to develop an ANN dosing algorithm. Data from another cohort of 856 were used to validate the algorithm. The clinical significance of the ANN dosing algorithm was evaluated by calculating the percentage of patients whose predicted dosage of warfarin was within 20 % of the actual stable therapeutic dose. The clinical significance was also compared with a previously published dosing algorithm.
resultsA feed-forward neural network with three layers was able to successfully predict the ideal warfarin dosage in 48 % of the patients. The neural network model explained 48 % and 43 % of the dosage variability observed among patients in the derivation and validation cohorts, respectively. ANN analysis identified several predictors of warfarin dosage including race; age; height; weight; cytochrome P450 (CYP)2C9 genotype; VKORC1 genotype; sulfonamide, azole antifungals, or macrolide administration; carbamazepine, phenytoin, or rifampicin administration; and amiodarone administration.
conclusionAn ANN was applied to develop a warfarin dosing algorithm. The proposed dosing algorithm has the potential to recommend warfarin dosages that are close to the appropriate dosages.
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