# Improving ICD coding using Chapter based Named Entities and Attentional Models

arXiv: https://arxiv.org/abs/2407.17230 · HTML: https://mlsanity.com/paper/2407.17230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [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)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [Automated Clinical Coding: What, Why, and Where We Are?](https://mlsanity.com/paper/2203.11092/) (2022)
- [A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries](https://mlsanity.com/paper/2107.10652/) (2021)
- [Deep learning models are not robust against noise in clinical text](https://mlsanity.com/paper/2108.12242/) (2021)
- [A Pseudo Label-wise Attention Network for Automatic ICD Coding](https://mlsanity.com/paper/2106.06822/) (2021)

## Abstract (arXiv)

Recent advancements in natural language processing (NLP) have led to automation in various domains. However, clinical NLP often relies on benchmark datasets that may not reflect real-world scenarios accurately. Automatic ICD coding, a vital NLP task, typically uses outdated and imbalanced datasets like MIMIC-III, with existing methods yielding micro-averaged F1 scores between 0.4 and 0.7 due to many false positives. Our research introduces an enhanced approach to ICD coding that improves F1 scores by using chapter-based named entities and attentional models. This method categorizes discharge summaries into ICD-9 Chapters and develops attentional models with chapter-specific data, eliminating the need to consider external data for code identification. For categorization, we use Chapter-IV to de-bias and influence key entities and weights without neural networks, creating accurate thresholds and providing interpretability for human validation. Post-validation, we develop attentional models for three frequent and three non-frequent codes from Chapter-IV using Bidirectional-Gated Recurrent Units (GRUs) with Attention and Transformer with Multi-head Attention architectures. The average Micro-F1 scores of 0.79 and 0.81 from these models demonstrate significant performance improvements in ICD coding.