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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Published in:PeerJ. Computer science Vol. 11; p. e2612
Main Authors: Wani, Aasim Ayaz, Abeer, Fatima
Format: Journal Article
Language:English
Published: United States PeerJ. Ltd 02.01.2025
PeerJ
PeerJ Inc
Subjects:
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
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Abstract 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.
AbstractList 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.
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.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.
ArticleNumber e2612
Audience Academic
Author Wani, Aasim Ayaz
Abeer, Fatima
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Keywords Deep learning
Dimensionality reduction
Variational autoencoders
Medical research
Machine learning
Missing value imputation
Generative adversarial networks
Artificial intelligence
Denoising autoencoders
Language English
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2025 Wani and Abeer.
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SubjectTerms 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
Title Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset
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