# A Label Attention Model for ICD Coding from Clinical Text

arXiv: https://arxiv.org/abs/2007.06351 · HTML: https://mlsanity.com/paper/2007.06351/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 30 of 48)

- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning](https://mlsanity.com/paper/2402.15700/) (2024)
- [De-identification is not enough: a comparison between de-identified and synthetic clinical notes](https://mlsanity.com/paper/2402.00179/) (2024)
- [Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding](https://mlsanity.com/paper/2204.10716/) (2022)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Description-based Label Attention Classifier for Explainable ICD-9 Classification](https://mlsanity.com/paper/2109.12026/) (2021)
- [Code Like Humans: A Multi-Agent Solution for Medical Coding](https://mlsanity.com/paper/2509.05378/) (2025)
- [DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction](https://mlsanity.com/paper/2310.07059/) (2023)
- [A General Knowledge Injection Framework for ICD Coding](https://mlsanity.com/paper/2505.18708/) (2025)
- [Serialized EHR make for good text representations](https://mlsanity.com/paper/2510.13843/) (2025)
- [Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review](https://mlsanity.com/paper/2412.18043/) (2024)
- [Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning](https://mlsanity.com/paper/2310.09672/) (2023)
- [Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification](https://mlsanity.com/paper/2405.19084/) (2024)
- [Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks](https://mlsanity.com/paper/2407.16166/) (2024)
- [MedDCR: Learning to Design Agentic Workflows for Medical Coding](https://mlsanity.com/paper/2511.13361/) (2025)
- [Graph Inference Towards ICD Coding](https://mlsanity.com/paper/2601.07496/) (2026)
- [TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding](https://mlsanity.com/paper/2510.15269/) (2025)
- [Can large language models be privacy preserving and fair medical coders?](https://mlsanity.com/paper/2412.05533/) (2024)
- [RuCCoD: Towards Automated ICD Coding in Russian](https://mlsanity.com/paper/2502.21263/) (2025)
- [Evaluation and LLM-Guided Learning of ICD Coding Rationales](https://mlsanity.com/paper/2508.16777/) (2025)
- [Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference](https://mlsanity.com/paper/2602.12542/) (2026)
- [ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding](https://mlsanity.com/paper/2605.08482/) (2026)
- [Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding](https://mlsanity.com/paper/2606.13940/) (2026)
- [KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs](https://mlsanity.com/paper/2608.20887/) (2026)
- [From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention](https://mlsanity.com/paper/2102.09136/) (2021)
- [ComplicaCode: Enhancing Disease Complication Detection in Electronic Health Records through ICD Path Generation](https://mlsanity.com/paper/2312.10259/) (2023)
- [Improving ICD coding using Chapter based Named Entities and Attentional Models](https://mlsanity.com/paper/2407.17230/) (2024)
- [Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank](https://mlsanity.com/paper/2410.23066/) (2024)
- [CRTRE: Causal Rule Generation with Target Trial Emulation Framework](https://mlsanity.com/paper/2411.06338/) (2024)

## Abstract (arXiv)

ICD coding is a process of assigning the International Classification of Disease diagnosis codes to clinical/medical notes documented by health professionals (e.g. clinicians). This process requires significant human resources, and thus is costly and prone to error. To handle the problem, machine learning has been utilized for automatic ICD coding. Previous state-of-the-art models were based on convolutional neural networks, using a single/several fixed window sizes. However, the lengths and interdependence between text fragments related to ICD codes in clinical text vary significantly, leading to the difficulty of deciding what the best window sizes are. In this paper, we propose a new label attention model for automatic ICD coding, which can handle both the various lengths and the interdependence of the ICD code related text fragments. Furthermore, as the majority of ICD codes are not frequently used, leading to the extremely imbalanced data issue, we additionally propose a hierarchical joint learning mechanism extending our label attention model to handle the issue, using the hierarchical relationships among the codes. Our label attention model achieves new state-of-the-art results on three benchmark MIMIC datasets, and the joint learning mechanism helps improve the performances for infrequent codes.