MedFiTRG: Jointly Learning Dynamic Temporal and Cross-Patient Graphs for Clinical Outcome Prediction

Shivani Gupta; Hari Om Kumar; Joydeep Chandra

IJCAI 20260 citations

MedFiTRG: Jointly Learning Dynamic Temporal and Cross-Patient Graphs for Clinical Outcome Prediction

Shivani Gupta, Hari Om Kumar, Joydeep Chandra

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. Extensive 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%). The code is available at https://anonymous.4open.science/r/MedFiTRG-2714

Clinical decision support system: Clinical decision support systemMedical diagnosis: Medical diagnosisHealth data mining: Health data miningMultimodal data: Multimodal dataExplainable AI: Explainable AI

BibTeX

@inproceedings{ijcai2026_medfitrgjointlyl,
  title = {MedFiTRG: Jointly Learning Dynamic Temporal and Cross-Patient Graphs for Clinical Outcome Prediction},
  author = {Shivani Gupta and Hari Om Kumar and Joydeep Chandra},
  booktitle = {IJCAI 2026},
  year = {2026}
}