# ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

arXiv: https://arxiv.org/abs/1912.00862 · HTML: https://mlsanity.com/paper/1912.00862/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 30 of 73)

- [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)
- [Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study](https://mlsanity.com/paper/2304.10909/) (2023)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [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)
- [Towards BERT-based Automatic ICD Coding: Limitations and Opportunities](https://mlsanity.com/paper/2104.06709/) (2021)
- [Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines](https://mlsanity.com/paper/2107.10650/) (2021)
- [ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes](https://mlsanity.com/paper/2403.05795/) (2024)
- [Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification](https://mlsanity.com/paper/2304.13998/) (2023)
- [CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning](https://mlsanity.com/paper/2402.15700/) (2024)
- [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)
- [Can GPT-3.5 Generate and Code Discharge Summaries?](https://mlsanity.com/paper/2401.13512/) (2024)
- [Code Like Humans: A Multi-Agent Solution for Medical Coding](https://mlsanity.com/paper/2509.05378/) (2025)
- [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)
- [A Two-Stage Decoder for Efficient ICD Coding](https://mlsanity.com/paper/2306.00005/) (2023)
- [Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings](https://mlsanity.com/paper/2402.03172/) (2024)
- [DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction](https://mlsanity.com/paper/2310.07059/) (2023)
- [Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation](https://mlsanity.com/paper/2405.19093/) (2024)
- [A General Knowledge Injection Framework for ICD Coding](https://mlsanity.com/paper/2505.18708/) (2025)
- [Three-level Hierarchical Transformer Networks for Long-sequence and Multiple Clinical Documents Classification](https://mlsanity.com/paper/2104.08444/) (2021)
- [HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding](https://mlsanity.com/paper/2208.02301/) (2022)
- [TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding](https://mlsanity.com/paper/2305.18576/) (2023)
- [FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction](https://mlsanity.com/paper/2406.11928/) (2024)
- [Multitask Recalibrated Aggregation Network for Medical Code Prediction](https://mlsanity.com/paper/2104.00952/) (2021)
- [Modeling Diagnostic Label Correlation for Automatic ICD Coding](https://mlsanity.com/paper/2106.12800/) (2021)

## Abstract (arXiv)

Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field. We evaluated the effectiveness of our model on the widely-used MIMIC dataset. On the full code set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of 6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set of MIMIC-II, our model outperformed all the existing and state-of-the-art models in all evaluation metrics. The code is available at https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network.