HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care

• URL: http://arxiv.org/abs/2606.05994v1
• Date: Thu, 04 Jun 2026 10:42:18 GMT
• Title: HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care
• Abstract summary: We propose HoT-SSM, a parameter efficient and higher-order temporal graph reasoning with state space models.<n>For each visit, HoT-SSM constructs hypergraphs by grouping semantically related clinical concepts into hyperedges using domain knowledge.<n>Experiments on MIMIC-III and MIMIC-IV datasets shows significant performance improvement over the current state-of-the-art models.
• Score: 4.959832547805051
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain. However, existing MKG-based approaches are limited in capturing pairwise relations between clinical concepts (e.g., conditions, procedures, and medications), and restricts their ability to model higher-order interactions among co-occurring or semantically related concepts. In addition, most representation learning methods that leverage MKGs either collapse temporal information across visits or lack an explicit mechanism for modeling long-range temporal dependencies, which is critical for clinical tasks such as mortality prediction. To mitigate these limitations, we propose HoT-SSM, a parameter efficient and higher-order temporal graph reasoning with state space models. For each visit, HoT-SSM constructs hypergraphs by grouping semantically related clinical concepts into hyperedges using domain knowledge, thereby preserving visit-level clinical context. Further, to model the temporal dynamics while learning the representations, we introduce a novel dynamic hypergraph-based state space model that explicitly captures patients latent state evolution over time while preserving long-range information. The learned representations are used for downstream clinical prediction and reasoning. Experiments on MIMIC-III and MIMIC-IV datasets shows significant performance improvement over the current state-of-the-art models, demonstrating the effectiveness of jointly modeling higher-order clinical interactions and long-range temporal dependencies.

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