Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.

Area of Science:

• Pharmacogenomics
• Computational Biology
• Clinical Pharmacology

Background:

• Warfarin is a widely used anticoagulant with a narrow therapeutic index, requiring close international normalized ratio (INR) monitoring.
• Dosing deviations can lead to serious thromboembolic or bleeding events.

Purpose of the Study:

• To review and synthesize the literature on machine learning (ML) approaches for warfarin dose individualization.
• To assess the predictive performance and clinical relevance of ML models in warfarin therapy.

Main Methods:

• Narrative review of studies employing machine learning techniques (e.g., support vector regression, neural networks, ensemble models, reinforcement learning) for warfarin dosing.
• Focus on models incorporating clinical and genetic factors.

Main Results:

• ML-based warfarin dosing models demonstrate potential for improved prediction of therapeutic doses and INR regulation compared to traditional methods.
• Existing ML models often suffer from small sample sizes, limited external validation, and methodological heterogeneity, impacting generalizability.

Conclusions:

• Artificial intelligence (AI) and ML offer potential advantages in warfarin dosing precision and INR control.
• Definitive conclusions on comparative effectiveness require further robust studies with larger sample sizes and rigorous external validation.