Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset

Warfarin, a commonly prescribed anticoagulant, poses significant dosing challenges due to its narrow therapeutic range and high variability in patient responses. This study applies advanced machine learning techniques to improve the accuracy of international normalized ratio (INR) predictions using...

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Veröffentlicht in:PeerJ. Computer science Jg. 11; S. e2612
Hauptverfasser: Wani, Aasim Ayaz, Abeer, Fatima
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States PeerJ. Ltd 02.01.2025
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Schlagworte:
Artificial Intelligence
Bioinformatics
Computational Biology
Computer Science
Data Mining and Machine Learning
Data Science
Deep learning
Denoising autoencoders
Dimensionality reduction
Dosage and administration
Drugs
Machine learning
Medical research
Neural networks
Warfarin
United States
Deep learning
Dimensionality reduction
Variational autoencoders
Medical research
Machine learning
Missing value imputation
Generative adversarial networks
Artificial intelligence
Denoising autoencoders
ISSN:2376-5992, 2376-5992
Online-Zugang:Volltext
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Zusammenfassung:Warfarin, a commonly prescribed anticoagulant, poses significant dosing challenges due to its narrow therapeutic range and high variability in patient responses. This study applies advanced machine learning techniques to improve the accuracy of international normalized ratio (INR) predictions using the MIMIC-III dataset, addressing the critical issue of missing data. By leveraging dimensionality reduction methods such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), and advanced imputation techniques including denoising autoencoders (DAE) and generative adversarial networks (GAN), we achieved significant improvements in predictive accuracy. The integration of these methods substantially reduced prediction errors compared to traditional approaches. This research demonstrates the potential of machine learning (ML) models to provide more personalized and precise dosing strategies that reduce the risks of adverse drug events. Our method could integrate into clinical workflows to enhance anticoagulation therapy in cases of missing data, with potential applications in other complex medical treatments.
Bibliographie:ObjectType-Article-1
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.2612