# Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification

arXiv: https://arxiv.org/abs/2304.13998 · HTML: https://mlsanity.com/paper/2304.13998/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [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)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [Inferring Degrees from Incomplete Networks and Nonlinear Dynamics](https://mlsanity.com/paper/2004.10546/) (2020)

## Built on by (most cited first, 19 of 19)

- [MedSyn: LLM-based Synthetic Medical Text Generation Framework](https://mlsanity.com/paper/2408.02056/) (2024)
- [CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning](https://mlsanity.com/paper/2402.15700/) (2024)
- [Can GPT-3.5 Generate and Code Discharge Summaries?](https://mlsanity.com/paper/2401.13512/) (2024)
- [PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records](https://mlsanity.com/paper/2307.02006/) (2023)
- [Surpassing GPT-4 Medical Coding with a Two-Stage Approach](https://mlsanity.com/paper/2311.13735/) (2023)
- [Uncertainty-aware abstention in medical diagnosis based on medical texts](https://mlsanity.com/paper/2502.18050/) (2025)
- [Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset](https://mlsanity.com/paper/2401.15290/) (2024)
- [Towards detecting unanticipated bias in Large Language Models](https://mlsanity.com/paper/2404.02650/) (2024)
- [Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning](https://mlsanity.com/paper/2410.13439/) (2024)
- [CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models](https://mlsanity.com/paper/2509.26136/) (2025)
- [SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis](https://mlsanity.com/paper/2511.11935/) (2025)
- [Automated Clinical Coding for Outpatient Departments](https://mlsanity.com/paper/2312.13533/) (2023)
- [Symphony for Medical Coding: A Next-Generation Agentic System for Scalable and Explainable Medical Coding](https://mlsanity.com/paper/2603.29709/) (2026)
- [Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank](https://mlsanity.com/paper/2410.23066/) (2024)
- [Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings](https://mlsanity.com/paper/2507.03122/) (2025)
- [TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration](https://mlsanity.com/paper/2510.15267/) (2025)
- [Enhancing Rare Codes via Probability-Biased Directed Graph Attention for Long-Tail ICD Coding](https://mlsanity.com/paper/2511.09559/) (2025)
- [Real-Time Mobile Video Analytics for Pre-arrival Emergency Medical Services](https://mlsanity.com/paper/2511.14119/) (2025)
- [Graph-Constrained Policy Learning for Extreme Clinical Code Prediction](https://mlsanity.com/paper/2607.11954/) (2026)

## Abstract (arXiv)

Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding. However, there is a lack of widely accepted benchmarks for automated ICD coding models based on large-scale public EHR data. This paper proposes a public benchmark suite for ICD-10 coding using a large EHR dataset derived from MIMIC-IV, the most recent public EHR dataset. We implement and compare several popular methods for ICD coding prediction tasks to standardize data preprocessing and establish a comprehensive ICD coding benchmark dataset. This approach fosters reproducibility and model comparison, accelerating progress toward employing automated ICD coding in future studies. Furthermore, we create a new ICD-9 benchmark using MIMIC-IV data, providing more data points and a higher number of ICD codes than MIMIC-III. Our open-source code offers easy access to data processing steps, benchmark creation, and experiment replication for those with MIMIC-IV access, providing insights, guidance, and protocols to efficiently develop ICD coding models.