mlsanityData to 14 Oct 2026

A Multimodal Data Processing Pipeline for MIMIC-IV Dataset

arXiv 2601.11606 ↗First posted 8 Jan 2026cs.LGcited by 3

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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.

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

21 of 47 references.

References · 21

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9 more
  1. 2024Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency carecited by 15
  2. 2023A Multidatabase ExTRaction PipEline (METRE) for Facile Cross Validation in Critical Care Researchcited by 4
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  4. 2024An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimationcited by 3
  5. 2025ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphscited by 3
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Modelscited by 1
  2. 2026Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecturecited by 0
  3. 2026A General Multimodal Probing Framework for Data Fusion and Prediction with Frozen Large Language Models: Evidence from Multimodal EHR Datacited by 0