Knowledge Graph-Based Effective Cardiovascular Disease Treatment Using BEHRT: A Deep Learning Approach with EHR Integration

Integrating deep learning into healthcare has significantly improved clinical predictions, personalized interventions, and patient outcomes. The ability of BEHRT (Bidirectional Encoder Representations from Transformers for Electronic Health Records) to model temporal dependencies and capture complex relationships from patient medical records makes it particularly effective for predicting disease progression and outcomes. By encoding sequential information from longitudinal electronic health records (EHRs), BEHRT can analyse a patient's health trajectory over time, enabling more accurate early disease detection and personalized treatment strategies. Its transformer-based architecture allows for understanding patient data, including diagnosis codes, treatments, and other clinical features, which are critical for tailored interventions. In this study, the MIMIC-III dataset was used, and the application of BEHRTs to the cardiovascular disease subset was demonstrated. This approach showed a significant advancement in clinical predictions. The model effectively processed the large, unstructured, and semi-structured data, identifying key trends in disease progression and enhancing its ability to predict future health outcomes for patients with cardiovascular conditions. Additionally, we used a knowledge graph to represent the model’s insights into cardiovascular disease treatment and medication pathways. This graph highlighted key relationships between treatments and patient outcomes, serving as a valuable tool for clinical decision-making. BEHRT’s scalability and precision in handling vast amounts of structured health data underscore its potential to improve diagnosis, treatment planning, and overall patient care. Its application in cardiovascular disease showcases its broader utility across multiple medical domains, contributing to better healthcare outcomes and more personalized interventions.