A disease potential-driven graph attention model for comorbidity risk prediction of hypertension

Hypertension is associated with an increased risk of serious complications, and the hazards are very serious. However, current methods for predicting comorbidity risks face the challenge that comorbidity prediction relying solely on data driven may lead to clinically implausible associations and reduce model interpretability. Also, how to capture the fusion features of patient and identify differences among them to facilitate risk prediction needs to be addressed. To overcome these challenges, we propose a Disease Potential-Driven Graph Attention (DP-GA) model for comorbidity risk prediction of hypertension, which has 3-fold ideas: (a) Constructing a fusion mechanism for the correlation among the patients' disease features and the structural, thus integrating feature attention and structural attention effectively; (b) Introducing a similarity-difference balance mechanism to further identify the relationships among patients; and (c) Designing a disease potential-driven attention mechanism to calculate the disease potential and construct masks, thus preserving the effective associations from high-risk patients to low-risk patients. Experimental results demonstrate that our proposed DP-GA model achieves a significant improvement in comorbidity risk prediction for patients with hypertension across three comorbidity datasets collected by the research group, compared with both the baseline and state-of-the-art peer methods. We also analyze the comorbidity network to predict the risk of hypertension comorbidity, thereby improving interpretability and early prediction of such comorbidities.