DHGL: Dynamic hypergraph-based deep learning model for disease prediction

Electronic health record (EHR) data is crucial in providing comprehensive historical disease information for patients and is frequently utilized in health event prediction. However, current deep learning models that rely on EHR data encounter significant challenges. These include inadequate exploration of higher-order relationships among diseases, a failure to capture dynamic relationships in existing relationship-based disease prediction models, and insufficient utilization of patient symptom information. To address these limitations, a novel dynamic HyperGraph-based deep learning model is introduced for disease prediction (DHGL) in this study. Initially, pertinent symptom information is extracted from patients to assign them with an initial embedding. Subsequently, sub-hypergraphs are constructed to consider distinct patient cohorts rather than treating them as isolated entities. Ultimately, these hypergraphs are dynamized to gain a more nuanced understanding of patient relationships. The evaluation of DHGL on real-world EHR datasets reveals its superiority over several state-of-the-art baseline methods in terms of predictive accuracy. A novel dynamic HyperGraph-based deep learning model is proposed for disease prediction (DHGL) here. The proposed DHGL model is evaluated on real-world electronic health record datasets and it is demonstrated that it outperforms several state-of-the-art baseline methods in terms of predictive accuracy. image