Abstract
BACKGROUND: Electronic health records (EHRs) contain comprehensive information regarding diagnoses, clinical procedures, and prescribed medications. This makes them a valuable resource for developing automated hypertension medication recommendation systems. Within this field, existing research has used machine learning approaches, leveraging demographic characteristics and basic clinical indicators, or deep learning techniques, which extract patterns from EHR data, to predict optimal medications or improve the accuracy of recommendations for common antihypertensive medication categories. However, these methodologies have significant limitations. They rarely adequately characterize the synergistic relationships among heterogeneous medical entities, such as the interplay between comorbid conditions, laboratory results, and specific antihypertensive agents. Furthermore, given the chronic and fluctuating nature of hypertension, effective medication recommendations require dynamic adaptation to disease progression over time. However, current approaches either lack rigorous temporal modeling of EHR data or fail to effectively integrate temporal dynamics with interentity relationships, resulting in the generation of recommendations that are not clinically appropriate due to the neglect of these critical factors. OBJECTIVE: This study aims to overcome the challenges in existing methods and introduce a novel model for hypertension medication recommendation that leverages the synergy and selectivity of heterogeneous medical entities. METHODS: First, we used patient EHR data to construct both heterogeneous and homogeneous graphs. The interentity synergies were captured using a multihead graph attention mechanism to enhance entity-level representations. Next, a bidirectional temporal selection mechanism calculated selective coefficients between current and historical visit records and aggregated them to form refined visit-level representations. Finally, medication recommendation probabilities were determined based on these comprehensive patient representations. RESULTS: Experimental evaluations on the real-world datasets Medical Information Mart for Intensive Care (MIMIC)-III v1.4 and MIMIC-IV v2.2 demonstrated that the proposed model achieved Jaccard similarity coefficients of 58.01% and 55.82%, respectively; areas under the curve of precision-recall of 83.56% and 80.69%, respectively; and F1-scores of 68.95% and 64.83%, respectively, outperforming the baseline models. CONCLUSIONS: The findings indicate the superior efficacy of the introduced model in medication recommendation, highlighting its potential to enhance clinical decision-making in the management of hypertension. The code for the model has been released on GitHub.