# A Multimodal Data Processing Pipeline for MIMIC-IV Dataset

arXiv: https://arxiv.org/abs/2601.11606 · HTML: https://mlsanity.com/paper/2601.11606/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care](https://mlsanity.com/paper/2407.17856/) (2024)
- [A Multidatabase ExTRaction PipEline (METRE) for Facile Cross Validation in Critical Care Research](https://mlsanity.com/paper/2302.13402/) (2023)
- [Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions](https://mlsanity.com/paper/2401.11252/) (2024)
- [An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation](https://mlsanity.com/paper/2412.10454/) (2024)
- [ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs](https://mlsanity.com/paper/2504.16394/) (2025)
- [PiCME: Pipeline for Contrastive Modality Evaluation and Encoding in the MIMIC Dataset](https://mlsanity.com/paper/2507.03165/) (2025)
- [HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning](https://mlsanity.com/paper/2509.24118/) (2025)
- [Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises](https://mlsanity.com/paper/2506.12808/) (2025)
- [Toward Revealing Nuanced Biases in Medical LLMs](https://mlsanity.com/paper/2507.21176/) (2025)

## Built on by (most cited first, 3 of 3)

- [MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models](https://mlsanity.com/paper/2607.07673/) (2026)
- [Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture](https://mlsanity.com/paper/2606.28623/) (2026)
- [A General Multimodal Probing Framework for Data Fusion and Prediction with Frozen Large Language Models: Evidence from Multimodal EHR Data](https://mlsanity.com/paper/2606.28798/) (2026)

## Abstract (arXiv)

The MIMIC-IV dataset is a large, publicly available electronic health record (EHR) resource widely used for clinical machine learning research. It comprises multiple modalities, including structured data, clinical notes, waveforms, and imaging data. Working with these disjointed modalities requires an extensive manual effort to preprocess and align them for downstream analysis. While several pipelines for MIMIC-IV data extraction are available, they target a small subset of modalities or do not fully support arbitrary downstream applications. In this work, we greatly expand our prior popular unimodal pipeline and present a comprehensive and customizable multimodal pipeline that can significantly reduce multimodal processing time and enhance the reproducibility of MIMIC-based studies. Our pipeline systematically integrates the listed modalities, enabling automated cohort selection, temporal alignment across modalities, and standardized multimodal output formats suitable for arbitrary static and time-series downstream applications. We release the code, a simple UI, and a Python package for selective integration (with embedding) at https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.