# CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning

arXiv: https://arxiv.org/abs/2402.15700 · HTML: https://mlsanity.com/paper/2402.15700/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [R-Drop: Regularized Dropout for Neural Networks](https://mlsanity.com/paper/2106.14448/) (2021)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding](https://mlsanity.com/paper/2203.01515/) (2022)
- [A Label Attention Model for ICD Coding from Clinical Text](https://mlsanity.com/paper/2007.06351/) (2020)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study](https://mlsanity.com/paper/2103.06511/) (2021)
- [Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation](https://mlsanity.com/paper/2010.15728/) (2020)
- [Towards BERT-based Automatic ICD Coding: Limitations and Opportunities](https://mlsanity.com/paper/2104.06709/) (2021)
- [Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification](https://mlsanity.com/paper/2304.13998/) (2023)
- [Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt](https://mlsanity.com/paper/2211.13813/) (2022)
- [Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding](https://mlsanity.com/paper/2204.10716/) (2022)
- [ICDBigBird: A Contextual Embedding Model for ICD Code Classification](https://mlsanity.com/paper/2204.10408/) (2022)
- [A Two-Stage Decoder for Efficient ICD Coding](https://mlsanity.com/paper/2306.00005/) (2023)
- [Modelling Temporal Document Sequences for Clinical ICD Coding](https://mlsanity.com/paper/2302.12666/) (2023)
- [TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding](https://mlsanity.com/paper/2305.18576/) (2023)

## Built on by (most cited first, 18 of 18)

- [A General Knowledge Injection Framework for ICD Coding](https://mlsanity.com/paper/2505.18708/) (2025)
- [The Anatomy of Evidence: An Investigation Into Explainable ICD Coding](https://mlsanity.com/paper/2507.01802/) (2025)
- [Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning](https://mlsanity.com/paper/2410.13439/) (2024)
- [MEDMKG: Benchmarking Medical Knowledge Exploitation with Multimodal Knowledge Graph](https://mlsanity.com/paper/2505.17214/) (2025)
- [A Novel ICD Coding Method Based on Associated and Hierarchical Code Description Distillation](https://mlsanity.com/paper/2404.11132/) (2024)
- [Evaluation and LLM-Guided Learning of ICD Coding Rationales](https://mlsanity.com/paper/2508.16777/) (2025)
- [BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models](https://mlsanity.com/paper/2410.09079/) (2024)
- [A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference](https://mlsanity.com/paper/2410.14236/) (2024)
- [Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank](https://mlsanity.com/paper/2410.23066/) (2024)
- [Data Augmentation Techniques for Chinese Disease Name Normalization](https://mlsanity.com/paper/2501.01195/) (2025)
- [MKE-Coder: Multi-Axial Knowledge with Evidence Verification in ICD Coding for Chinese EMRs](https://mlsanity.com/paper/2502.14916/) (2025)
- [TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration](https://mlsanity.com/paper/2510.15267/) (2025)
- [Enhancing Rare Codes via Probability-Biased Directed Graph Attention for Long-Tail ICD Coding](https://mlsanity.com/paper/2511.09559/) (2025)
- [From Documents to Spans: Span-Centric Learning for LLM-based Explainable Medical Coding](https://mlsanity.com/paper/2603.15270/) (2026)
- [Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codes](https://mlsanity.com/paper/2605.17755/) (2026)
- [xMICD: Explainable Representation of Multiple ICD Codes](https://mlsanity.com/paper/2608.00935/) (2026)
- [CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding](https://mlsanity.com/paper/2608.30234/) (2026)
- [Beyond ICD Codes: Semantic IDs for Generative Medical Coding](https://mlsanity.com/paper/2610.19301/) (2026)

## Abstract (arXiv)

Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach, unlike existing methods, employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. We evaluate our approach on six public ICD coding datasets and the experimental results demonstrate the effectiveness of our approach compared to state-of-the-art baselines.