# Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text

arXiv: https://arxiv.org/abs/2009.14578 · HTML: https://mlsanity.com/paper/2009.14578/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 21 of 21)

- [Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study](https://mlsanity.com/paper/2103.06511/) (2021)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines](https://mlsanity.com/paper/2107.10650/) (2021)
- [Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt](https://mlsanity.com/paper/2211.13813/) (2022)
- [Exploring LLM Multi-Agents for ICD Coding](https://mlsanity.com/paper/2406.15363/) (2024)
- [Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding](https://mlsanity.com/paper/2204.10716/) (2022)
- [A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries](https://mlsanity.com/paper/2107.10652/) (2021)
- [ICDBigBird: A Contextual Embedding Model for ICD Code Classification](https://mlsanity.com/paper/2204.10408/) (2022)
- [PyHealth: A Python Library for Health Predictive Models](https://mlsanity.com/paper/2101.04209/) (2021)
- [Multitask Balanced and Recalibrated Network for Medical Code Prediction](https://mlsanity.com/paper/2109.02418/) (2021)
- [Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation](https://mlsanity.com/paper/2405.19093/) (2024)
- [Multitask Recalibrated Aggregation Network for Medical Code Prediction](https://mlsanity.com/paper/2104.00952/) (2021)
- [Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models](https://mlsanity.com/paper/2212.05857/) (2022)
- [HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding](https://mlsanity.com/paper/2212.04891/) (2022)
- [Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification](https://mlsanity.com/paper/2405.19084/) (2024)
- [Active learning for medical code assignment](https://mlsanity.com/paper/2104.05741/) (2021)
- [Parameter Selection: Why We Should Pay More Attention to It](https://mlsanity.com/paper/2107.05393/) (2021)
- [Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning](https://mlsanity.com/paper/2109.03062/) (2021)
- [NoteContrast: Contrastive Language-Diagnostic Pretraining for Medical Text](https://mlsanity.com/paper/2412.11477/) (2024)
- [LTR-ICD: A Ranking-Aware Framework for Automatic ICD Coding](https://mlsanity.com/paper/2510.13922/) (2025)
- [CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding](https://mlsanity.com/paper/2608.30234/) (2026)

## Abstract (arXiv)

Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems. The emergence of deep models in natural language processing has boosted the development of automatic assignment methods. However, recent advanced neural architectures with flat convolutions or multi-channel feature concatenation ignore the sequential causal constraint within a text sequence and may not learn meaningful clinical text representations, especially for lengthy clinical notes with long-term sequential dependency. This paper proposes a Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size. Experiments on a real-world clinical dataset empirically show that our model improves the state of the art.