mlsanityData to 14 Oct 2026

Graph Inference Towards ICD Coding

arXiv 2601.07496 ↗First posted 12 Jan 2026cs.LG cs.AIcited by 3

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Abstract (arXiv)

Automated ICD coding involves assigning standardized diagnostic codes to clinical narratives. The vast label space and extreme class imbalance continue to challenge precise prediction. To address these issues, LabGraph is introduced -- a unified framework that reformulates ICD coding as a graph generation task. By combining adversarial domain adaptation, graph-based reinforcement learning, and perturbation regularization, LabGraph effectively enhances model robustness and generalization. In addition, a label graph discriminator dynamically evaluates each generated code, providing adaptive reward feedback during training. Experiments on benchmark datasets demonstrate that LabGraph consistently outperforms previous approaches on micro-F1, micro-AUC, and P@K.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 12 Jan 2026.

Builds on

7 of 23 references.

References · 7

  1. 2017Graph Attention Networkscited by 8,923
  2. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  3. 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 254
  4. 2022Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Codingcited by 53
  5. 2020A Label Attention Model for ICD Coding from Clinical Textcited by 48
  6. 2020Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisationcited by 38
  7. 2023Description-Enhanced Label Embedding Contrastive Learning for Text Classificationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Controlcited by 0
  2. 2026Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Frameworkcited by 0
  3. 2026FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Driftcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Graph-Constrained Policy Learning for Extreme Clinical Code Prediction
  2. 2021GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
  3. 2026Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding
  4. 2026Foundation Agents Meet Agentic Deep Research: Evidence-Grounded Clinical Code Forecasting
  5. 2026Enhancing Visual Reasoning in Chest X-Ray Report Generation Using Reinforcement Learning
  6. 2026HyperWalker: Dynamic Hypergraph-Based Deep Diagnosis for Multi-Hop Clinical Modeling across EHR and X-Ray in Medical VLMs
  7. 2024Exploring LLM Multi-Agents for ICD Coding
  8. 2025G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning