{"type":"ScholarlyArticle","title":"MedFiTRG: Jointly Learning Dynamic Temporal and Cross-Patient Graphs for Clinical Outcome Prediction","authors":[{"name":"Shivani Gupta","slug":"shivani-gupta","given":"Shivani","family":"Gupta","affiliation":""},{"name":"Hari Om Kumar","slug":"hari-om-kumar","given":"Hari Om","family":"Kumar","affiliation":""},{"name":"Joydeep Chandra","slug":"joydeep-chandra","given":"Joydeep","family":"Chandra","affiliation":""}],"abstract":"Integrating heterogeneous clinical modalities, structured electronic health records (EHRs), clinical text, and medical imaging is crucial for reliable clinical prediction, yet real-world data are often sparse and imbalanced. Furthermore, prior approaches treat temporal dynamics and inter-patient relationships in isolation, overlooking the dynamic interaction of patient trajectories across populations. We introduce a modality-enhanced dynamic temporal relational graph (MedFiTRG), a unified framework that jointly models sparsity, temporal dynamics, and cross-patient relational dependencies. MedFiTRG leverages modulated graph neural networks (MGNN) to learn modality-aware embeddings, enabling meaningful representation of sparse modalities through adaptive feature modulation. These embeddings are integrated into a temporal relational graph (TRG), where directed intra-patient edges capture longitudinal progression and dynamic inter-patient edges model population-level similarities for synchronized temporal-relational reasoning.\r\nExtensive experiments on large-scale real-world datasets across four clinical tasks demonstrate that MedFiTRG achieves superior or comparable performance against state-of-the-art baselines, improving Macro-F1 from 0.155 to 0.310 for length of stay (LOS) classification and achieving an AUROC of 0.939 for mortality prediction (↑6.45%).\r\nThe code is available at https://anonymous.4open.science/r/MedFiTRG-2714","doi":"10.24963/ijcai.2026/746","url":"https://ijcai.org/proceedings/2026/746","pdf":"https://ijcai.org/proceedings/2026/0746.pdf","markdown":"https://ijcai.org/proceedings/2026/746.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/746","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":746,"pages":{"first":6707,"last":6715},"keywords":["Health data mining: Health data mining","Multimodal data: Multimodal data","Explainable AI: Explainable AI","Medical diagnosis: Medical diagnosis","Clinical decision support system: Clinical decision support system"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}