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

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 approach

SUBMITTER: Wani AA 

PROVIDER: S-EPMC11784795 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature