# Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings

arXiv: https://arxiv.org/abs/2402.03172 · HTML: https://mlsanity.com/paper/2402.03172/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [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)
- [Revisiting Transformer-based Models for Long Document Classification](https://mlsanity.com/paper/2204.06683/) (2022)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [A Comparative Evaluation of Quantification Methods](https://mlsanity.com/paper/2103.03223/) (2021)

## Built on by (most cited first, 9 of 9)

- [TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding](https://mlsanity.com/paper/2510.15269/) (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)
- [A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference](https://mlsanity.com/paper/2410.14236/) (2024)
- [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)
- [LearnActCoder: Role-Aware Error Memory for Adaptive Clinical Coding Agents](https://mlsanity.com/paper/2609.19721/) (2026)

## Abstract (arXiv)

Although the International Classification of Diseases (ICD) has been adopted worldwide, manually assigning ICD codes to clinical text is time-consuming, error-prone, and expensive, motivating the development of automated approaches. This paper describes a novel approach for automated ICD coding, combining several ideas from previous related work. We specifically employ a strong Transformer-based model as a text encoder and, to handle lengthy clinical narratives, we explored either (a) adapting the base encoder model into a Longformer, or (b) dividing the text into chunks and processing each chunk independently. The representations produced by the encoder are combined with a label embedding mechanism that explores diverse ICD code synonyms. Experiments with different splits of the MIMIC-III dataset show that the proposed approach outperforms the current state-of-the-art models in ICD coding, with the label embeddings significantly contributing to the good performance. Our approach also leads to properly calibrated classification results, which can effectively inform downstream tasks such as quantification.