Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation

2025-05-12

AI Summary

This paper addresses the critical clinical decision problem of “which medical examinations should be performed?” by proposing the first intelligent method for medical test recommendation. Existing healthcare recommender systems predominantly focus on treatment, neglecting the complexity of diagnostic test selection—particularly the irregular spatiotemporal dependencies embedded in heterogeneous and redundant patient records. To tackle this, we introduce a two-stage paradigm: (1) a task-adaptive diffusion model for noise-robust denoising of examination sequences; and (2) a learnable-basis-function-driven spatiotemporal graph KANsformer that jointly integrates heterogeneous graph representations with Kolmogorov–Arnold Networks to model high-order dynamic associations. We also construct the first publicly available dataset for medical test recommendation. Extensive experiments demonstrate that our method achieves state-of-the-art performance on this benchmark, significantly outperforming mainstream recommendation and graph-based models, while ensuring both high accuracy and clinical interpretability.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recommendation systems in AI-based medical diagnostics and treatment constitute a critical component of AI in healthcare. Although some studies have explored this area and made notable progress, healthcare recommendation systems remain in their nascent stage. And these researches mainly target the treatment process such as drug or disease recommendations. In addition to the treatment process, the diagnostic process, particularly determining which medical examinations are necessary to evaluate the condition, also urgently requires intelligent decision support. To bridge this gap, we first formalize the task of medical examination recommendations. Compared to traditional recommendations, the medical examination recommendation involves more complex interactions. This complexity arises from two folds: 1) The historical medical records for examination recommendations are heterogeneous and redundant, which makes the recommendation results susceptible to noise. 2) The correlation between the medical history of patients is often irregular, making it challenging to model spatiotemporal dependencies. Motivated by the above observation, we propose a novel Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation (DST-GKAN) with a two-stage learning paradigm to solve the above challenges. In the first stage, we exploit a task-adaptive diffusion model to distill recommendation-oriented information by reducing the noises in heterogeneous medical data. In the second stage, a spatiotemporal graph KANsformer is proposed to simultaneously model the complex spatial and temporal relationships. Moreover, to facilitate the medical examination recommendation research, we introduce a comprehensive dataset. The experimental results demonstrate the state-of-the-art performance of the proposed method compared to various competitive baselines.

Problem

• Proposes AI system for medical exam recommendations in diagnostics
• Addresses noise in heterogeneous medical data via diffusion model
• Models spatiotemporal patient history with graph KANsformer

Innovation

• Task-adaptive diffusion model reduces noise
• Spatiotemporal graph KANsformer models relationships
• Two-stage learning paradigm enhances recommendations