{"type":"ScholarlyArticle","title":"Patient-Visit-Spanned Hypergraph Learning for EHR-based Diagnosis Prediction","authors":[{"name":"Ye Yuan","slug":"ye-yuan","given":"Ye","family":"Yuan","affiliation":""},{"name":"Haiyan Wang","slug":"haiyan-wang","given":"Haiyan","family":"Wang","affiliation":""},{"name":"Lun Hu","slug":"lun-hu","given":"Lun","family":"Hu","affiliation":""},{"name":"Xin Luo","slug":"xin-luo","given":"Xin","family":"Luo","affiliation":""}],"abstract":"Hypergraphs effectively model complex interactions in structured Electronic Health Records (EHR). Consequently, Hypergraph Neural Networks (HGNNs) are commonly applied to EHR-based diagnosis prediction. However, existing HGNNs struggle to capture patient-visit long-range dependencies when processing EHR-derived hypergraphs. To tackle this issue, we propose a Patient-Visit-Spanned HyperGraph Learning (PVHGL) framework specifically designed for diagnosis prediction. Concretely, PVHGL initially constructs a unified patient-visit hypergraph that integrates visit records from all patients, enabling the capture of shared healthcare patterns across the patient population. Subsequently, it incorporates a Transformer architecture enhanced by two structure-encoding matrices to facilitate one-step message propagation, which preserves both local and global hypergraph structural properties effectively. Additionally, the framework integrates a medical code co-occurrence matrix to explicitly guide the learning process by highlighting critical medical code interactions. Comprehensive experiments on three real-world datasets demonstrate that the proposed PVHGL significantly outperforms state-of-the-art baselines in diagnosis prediction.","doi":"10.24963/ijcai.2026/780","url":"https://ijcai.org/proceedings/2026/780","pdf":"https://ijcai.org/proceedings/2026/0780.pdf","markdown":"https://ijcai.org/proceedings/2026/780.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/780","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Health","track":"AI and Health","number":780,"pages":{"first":7010,"last":7018},"keywords":["EHR analysis: EHR analysis","Medical diagnosis: Medical diagnosis","Clinical decision support system: Clinical decision support system"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}