# LTR-ICD: A Ranking-Aware Framework for Automatic ICD Coding

arXiv: https://arxiv.org/abs/2510.13922 · HTML: https://mlsanity.com/paper/2510.13922/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://mlsanity.com/paper/2112.07916/) (2021)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [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)
- [A Label Attention Model for ICD Coding from Clinical Text](https://mlsanity.com/paper/2007.06351/) (2020)
- [BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining](https://mlsanity.com/paper/2006.03685/) (2020)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text](https://mlsanity.com/paper/2009.14578/) (2020)
- [Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt](https://mlsanity.com/paper/2211.13813/) (2022)
- [Code Like Humans: A Multi-Agent Solution for Medical Coding](https://mlsanity.com/paper/2509.05378/) (2025)
- [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)

## Abstract (arXiv)

Clinical notes contain unstructured text provided by clinicians during patient encounters. These notes are usually accompanied by a sequence of diagnostic codes following the International Classification of Diseases (ICD). Correctly assigning and ordering ICD codes is essential for medical diagnosis and reimbursement. However, automating this task remains challenging. State-of-the-art methods treated this problem as a classification task, leading to ignoring the order of ICD codes that is essential for different purposes. In this work, as a first attempt, we approach this task from a retrieval system perspective to consider the order of codes, thus formulating this problem as a classification and ranking task. Our results and analysis show that the proposed framework has a superior ability to identify high-priority codes compared to other methods. For instance, our model's accuracy in correctly ranking primary diagnosis codes is 47%, compared to 20% for the state-of-the-art classifier. Additionally, in terms of classification metrics, the proposed model achieves a micro- and macro-F1 scores of 0.6065 and 0.2904, respectively, surpassing the previous best model with scores of 0.6035 and 0.2741.