Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation

TL;DR

This paper introduces DST-GKAN, a novel two-stage model combining diffusion and spatiotemporal graph transformers to improve medical examination recommendations by handling complex, noisy, and irregular patient data.

Contribution

The paper proposes a new diffusion-driven spatiotemporal graph transformer model with a two-stage learning paradigm for medical examination recommendation, addressing data noise and irregular correlations.

Findings

Achieves state-of-the-art performance on a new comprehensive dataset.

Effectively reduces noise in heterogeneous medical data.

Models complex spatiotemporal relationships in patient histories.

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.