Leveraging Federated Learning for Automatic Detection of Clopidogrel
  Treatment Failures
Samuel Kim Min Sang Kim
The effectiveness of clopidogrel, a widely used antiplatelet medication,
varies significantly among individuals, necessitating the development of
precise predictive models to optimize patient care. In this study, we leverage
federated learning strategies to address clopidogrel treatment failure
detection. Our research harnesses the collaborative power of multiple
healthcare institutions, allowing them to jointly train machine learning models
while safeguarding sensitive patient data. Utilizing the UK Biobank dataset,
which encompasses a vast and diverse population, we partitioned the data based
on geographic centers and evaluated the performance of federated learning. Our
results show that while centralized training achieves higher Area Under the
Curve (AUC) values and faster convergence, federated learning approaches can
substantially narrow this performance gap. Our findings underscore the
potential of federated learning in addressing clopidogrel treatment failure
detection, offering a promising avenue for enhancing patient care through
personalized treatment strategies while respecting data privacy. This study
contributes to the growing body of research on federated learning in healthcare
and lays the groundwork for secure and privacy-preserving predictive models for
various medical conditions.