# An Automatic ICD Coding Network Using Partition-Based Label Attention

arXiv: https://arxiv.org/abs/2211.08429 · HTML: https://mlsanity.com/paper/2211.08429/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Hierarchical Transformers for Long Document Classification](https://mlsanity.com/paper/1910.10781/) (2019)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences](https://mlsanity.com/paper/2201.11838/) (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 Automated ICD Coding Using Deep Learning](https://mlsanity.com/paper/1711.04075/) (2017)
- [Inferring Degrees from Incomplete Networks and Nonlinear Dynamics](https://mlsanity.com/paper/2004.10546/) (2020)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding](https://mlsanity.com/paper/2104.10652/) (2021)
- [Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding](https://mlsanity.com/paper/2204.10716/) (2022)
- [Multitask Balanced and Recalibrated Network for Medical Code Prediction](https://mlsanity.com/paper/2109.02418/) (2021)

## Built on by (most cited first, 2 of 2)

- [Can GPT-3.5 Generate and Code Discharge Summaries?](https://mlsanity.com/paper/2401.13512/) (2024)
- [Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records](https://mlsanity.com/paper/2405.11622/) (2024)

## Abstract (arXiv)

International Classification of Diseases (ICD) is a global medical classification system which provides unique codes for diagnoses and procedures appropriate to a patient's clinical record. However, manual coding by human coders is expensive and error-prone. Automatic ICD coding has the potential to solve this problem. With the advancement of deep learning technologies, many deep learning-based methods for automatic ICD coding are being developed. In particular, a label attention mechanism is effective for multi-label classification, i.e., the ICD coding. It effectively obtains the label-specific representations from the input clinical records. However, because the existing label attention mechanism finds key tokens in the entire text at once, the important information dispersed in each paragraph may be omitted from the attention map. To overcome this, we propose a novel neural network architecture composed of two parts of encoders and two kinds of label attention layers. The input text is segmentally encoded in the former encoder and integrated by the follower. Then, the conventional and partition-based label attention mechanisms extract important global and local feature representations. Our classifier effectively integrates them to enhance the ICD coding performance. We verified the proposed method using the MIMIC-III, a benchmark dataset of the ICD coding. Our results show that our network improves the ICD coding performance based on the partition-based mechanism.