mlsanityData to 7 Oct 2026

Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits

arXiv 2408.07084 ↗First posted 8 Aug 2024cs.LG cs.AIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 8 Aug 2024.

Builds on

19 of 29 references.

References · 19

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  2. 2018Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNNcited by 142
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7 more
  1. 2024Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodulescited by 7
  2. 2024Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Techniquecited by 7
  3. 2024Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learningcited by 6
  4. 2024Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technologycited by 6
  5. 2024Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learningcited by 5
  6. 2024Research on Feature Extraction Data Processing System For MRI of Brain Diseases Based on Computer Deep Learningcited by 5
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 11; 2025: 0; 2026: 1.

12 papers.

  1. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  2. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  3. 2024Contrastive Learning for Knowledge-Based Question Generation in Large Language Modelscited by 8
  4. 2024Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Studycited by 8
  5. 2024Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Trainingcited by 7
  6. 2024Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysiscited by 7
  7. 2024Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentationcited by 6
  8. 2024Graph Neural Network Framework for Sentiment Analysis Using Syntactic Featurecited by 6
  9. 2024Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detectioncited by 5
  10. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5
  11. 2024Few-Shot Learning with Adaptive Weight Masking in Conditional GANscited by 4
  12. 2026The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Recordscited by 3