Deep Learning-Based Analysis of Electronic Health Records to Personalize Traditional Chinese Medicine Approaches for Hypertension Management

Zhenyi Tan
Cardiovascular Medicine Department, Hunan University of Chinese Medicine , Changsha 410208, Hunan, China
Wen Cao
Oncology and Hematology Department, Hunan University of Chinese Medicine, Changchun 410208, Hunan, China
Xiaoping Peng
Cardiovascular Medicine Department, The First Hospital affiliated to Hunan Traditional Chinese Medical College, Zhuzhou 412000, China

Abstract:

Hypertension is a global health challenge that requires effective management and personalized treatment approaches. Traditional Chinese Medicine (TCM) has long been utilized in the management of hypertension, but its integration with modern healthcare practices, particularly through the use of Electronic Health Records (EHR) and advanced technologies like deep learning, remains underexplored. This study aims to develop a deep learning-based approach to analyze EHR data and personalize TCM interventions for hypertension management. By leveraging EHR data, including patient demographics, medical histories, and lab results, deep learning models can predict optimal TCM treatments tailored to individual patient needs. This research explores the potential of combining machine learning techniques with traditional medicine to enhance personalized care, improve treatment outcomes, and bridge the gap between Western and Eastern medical practices. Results demonstrate that deep learning models can successfully identify patient-specific TCM interventions, offering a promising direction for the future of hypertension treatment. The study also highlights the challenges of integrating traditional medicine with data-driven healthcare solutions and suggests future research avenues for refining these models.