HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care
arXiv:2606.05994v1 Announce Type: new 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
The proliferation of medical knowledge graphs and the recognition of limitations in current AI models for capturing complex clinical relationships are driving research into higher-order reasoning methods.
Advanced AI models capable of nuanced interpretation of electronic health records can lead to more accurate diagnoses, personalized treatments, and improved healthcare efficiency, impacting patient outcomes and operational costs.
The ability of AI systems to move beyond simple pairwise relations in medical data to model complex, higher-order interactions changes how medical knowledge is leveraged for predictive analytics and clinical decision support.
- · AI healthcare technology providers
- · Hospitals and medical research institutions
- · Patients with complex conditions
- · Data scientists in healthcare
- · Legacy medical AI systems
- · Companies reliant on simple data models
Improved interpretability and accuracy of AI predictions in healthcare through sophisticated knowledge graph reasoning.
Development of new clinical decision support tools and personalized medicine platforms based on these advanced AI capabilities.
Potential for AI to identify novel disease pathways or drug interactions by uncovering previously unmodeled complex relationships within medical data.
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