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.

翻译：氯吡格雷作为一种广泛使用的抗血小板药物，其疗效在个体间存在显著差异，因此需要开发精确的预测模型来优化患者护理。在本研究中，我们利用联邦学习策略来应对氯吡格雷治疗失败的检测问题。我们的研究汇聚了多家医疗机构的协作力量，使它们能够在保护敏感患者数据的同时，联合训练机器学习模型。利用涵盖广泛多样人群的英国生物银行数据集，我们根据地理中心对数据进行分区，并评估了联邦学习的性能。结果表明，虽然集中式训练能获得更高的曲线下面积值并实现更快的收敛，但联邦学习方法能够显著缩小这一性能差距。我们的发现强调了联邦学习在检测氯吡格雷治疗失败方面的潜力，为通过个性化治疗策略提升患者护理同时尊重数据隐私提供了一条有前景的途径。本研究为联邦学习在医疗领域日益增多的研究成果做出了贡献，并为构建各类医疗状况下安全且保护隐私的预测模型奠定了基础。