# A Two-Stage Decoder for Efficient ICD Coding

arXiv: https://arxiv.org/abs/2306.00005 · HTML: https://mlsanity.com/paper/2306.00005/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [PLM-ICD: Automatic ICD Coding with Pretrained Language Models](https://mlsanity.com/paper/2207.05289/) (2022)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [Inferring Degrees from Incomplete Networks and Nonlinear Dynamics](https://mlsanity.com/paper/2004.10546/) (2020)
- [Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines](https://mlsanity.com/paper/2107.10650/) (2021)
- [Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding](https://mlsanity.com/paper/2204.10716/) (2022)

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

- [CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning](https://mlsanity.com/paper/2402.15700/) (2024)
- [Surpassing GPT-4 Medical Coding with a Two-Stage Approach](https://mlsanity.com/paper/2311.13735/) (2023)
- [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)
- [Evaluating Differentially Private Generation of Domain-Specific Text](https://mlsanity.com/paper/2508.20452/) (2025)
- [CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models](https://mlsanity.com/paper/2509.26136/) (2025)
- [Automated Clinical Coding for Outpatient Departments](https://mlsanity.com/paper/2312.13533/) (2023)
- [Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs](https://mlsanity.com/paper/2509.09699/) (2025)
- [Enhancing Rare Codes via Probability-Biased Directed Graph Attention for Long-Tail ICD Coding](https://mlsanity.com/paper/2511.09559/) (2025)
- [Beyond ICD Codes: Semantic IDs for Generative Medical Coding](https://mlsanity.com/paper/2610.19301/) (2026)

## Abstract (arXiv)

Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures. ICD coding is a challenging multilabel text classification problem due to noisy clinical document inputs and long-tailed label distribution. Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. However, most of them do not reflect how human coders generate the code: first, the coders select general code categories and then look for specific subcategories that are relevant to a patient's condition. Inspired by this, we propose a two-stage decoding mechanism to predict ICD codes. Our model uses the hierarchical properties of the codes to split the prediction into two steps: At first, we predict the parent code and then predict the child code based on the previous prediction. Experiments on the public MIMIC-III data set show that our model performs well in single-model settings without external data or knowledge.