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Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits

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arxiv 2408.07084 v3 pith:RBZK47CW submitted 2024-08-08 cs.LG cs.AI

Wangying Yang , Zitao Zheng , Zhizhong Wu , Bo Zhang , Yuanfang Yang This is my paper · ORCID
classification cs.LGcs.AI
keywords dhcedynamicmedicalmodelnetworksdiseasesneuralpatient
verification ladder T0 review T1 audit T2 compute T3 formal
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This study introduces a pioneering Dynamic Hypergraph Networks (DHCE) model designed to predict future medical diagnoses from electronic health records with enhanced accuracy. The DHCE model innovates by identifying and differentiating acute and chronic diseases within a patient's visit history, constructing dynamic hypergraphs that capture the complex, high-order interactions between diseases. It surpasses traditional recurrent neural networks and graph neural networks by effectively integrating clinical event data, reflected through medical language model-assisted encoding, into a robust patient representation. Through extensive experiments on two benchmark datasets, MIMIC-III and MIMIC-IV, the DHCE model exhibits superior performance, significantly outpacing established baseline models in the precision of sequential diagnosis prediction.

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Cited by 3 Pith papers

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  2. Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification

    cs.CV 2024-11 reject novelty 3.0 of 10

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  3. Few-Shot Learning with Adaptive Weight Masking in Conditional GANs

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