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Experimental results show the pipeline outperforms baselines in ranking, diversity, and explanations, proving that combining graph embeddings with agentic reasoning successfully bridges long-term profiles and immediate intent.
BACKGROUND Graph-based machine learning approaches, including Knowledge Graph Embedding (KGE) methods and Graph Neural Networks (GNNs), have emerged as powerful tools for modeling complex biomedical data. However, a systematic and clinically grounded comparison of these approaches across heterogeneous healthcare graphs, accounting for both predictive performance and real-world deployment constraints, is still lacking. METHODS We introduce AMIUGraph, a comprehensive benchmarking framework for healthcare link prediction that integrates real-world clinical data with external biomedical knowledge bases. AMIUGraph evaluates eight state-of-the-art models, of which four are knowledge graph embedding (KGE) methods DistMult, CP, ComplEx, and ConvE and four are graph neural network (GNN) architectures GCN, GraphSAGE, GAT, and GIN. The models are evaluated across three heterogeneous bipartite graphs representing Patients-Diseases, Diseases-Drugs, and Drugs-Targets interactions. Models are assessed under both transductive and inductive learning settings using accuracy, AUC, precision, recall, F1-score, and training time as evaluation metrics. RESULTS Experimental results show that model performance is strongly influenced by graph structure and sparsity. GNNs consistently achieve superior predictive performance on sparse interaction graphs, particularly for Diseases-Drugs and Drugs-Targets prediction tasks. In contrast, KGE models demonstrate competitive accuracy with substantially lower computational costs in inductive clinical scenarios involving unseen patients. These trends are especially relevant in clinically realistic settings characterized by multimorbidity, such as gastrointestinal and liver diseases, where frequent patient updates and complex therapeutic interactions are common. CONCLUSION AMIUGraph provides a clinically grounded and utility-driven benchmarking framework that jointly evaluates KGE and GNN models across multiple healthcare graph types and learning settings. The findings offer practical guidance for selecting graph-based models in medical decision-support systems, including applications in gastrointestinal healthcare, while promoting transparency and reproducibility through the public release of all datasets, protocols, and code.
It is demonstrated that ORM-based verification provides a simple, effective, and scalable alternative to heuristic test-time selection strategies for Text-to-SQL, and that ORMs scale effectively with larger candidate sets and yield stronger improvements on complex queries.
As Large Language Models (LLMs) become foundational to next-generation Intelligent Information Systems, the bridge between natural language interfaces and structured database systems remains a critical bottleneck. While Text-to-SQL generation enables cooperative support for complex query formulation, ensuring the reliability of these generated queries at inference time is a central challenge. Conventional methods rely on coarse execution-based signals, which may limit their ability to capture the nuanced semantic alignment required for high-stakes database environments. In this work, we propose the use of Outcome Reward Models (ORMs) as a fine-grained, probabilistic feedback mechanism for test-time verification in Text-to-SQL tasks. We introduce GradeSQL, a framework for training task-specific ORMs that assign scalar utility scores to candidate SQL queries based on their semantic correctness and alignment with database schema. Our approach is evaluated on the BIRD and Spider benchmarks across multiple open-source LLM families. Experimental results demonstrate that ORM-based verification consistently outperforms traditional execution-based heuristics.
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