Clopidogrel Treatment Failure

Clopidogrel treatment failure, characterized by the ineffective prevention of blood clots despite clopidogrel prescription, is a significant clinical challenge. Research focuses on developing accurate predictive models using machine learning, particularly leveraging electronic health records and employing algorithms like transformers (e.g., BERT) and time-series models to analyze longitudinal patient data. Data augmentation techniques are being explored to address the limitations of sparse labeled data, and federated learning offers a promising approach to improve model performance while protecting patient privacy. Improved prediction of treatment failure could lead to more personalized medication strategies and ultimately better patient outcomes.