构建可定制的多模态数据流水线，高效处理MIMIC-IV临床数据
A Multimodal Data Processing Pipeline for MIMIC-IV Dataset
- 整合结构化数据、病历文本、波形与影像多模态数据
- 实现自动队列筛选与跨模态时间对齐，提升处理效率
- 支持任意静态与时序下游任务，适合临床机器学习研究者
MIMIC-IV 是一个大型公开电子健康记录（EHR）资源，广泛用于临床机器学习研究。其包含结构化数据、临床笔记、波形和影像等多种模态。现有处理方法通常仅覆盖部分模态，或无法支持任意下游应用。本文在先前单模态流水线基础上，提出一个全面且可定制的多模态数据处理流程，显著减少多模态数据处理时间，并提高基于MIMIC的研究可复现性。该流程系统整合各类模态，支持自动化队列选择、跨模态时间对齐及标准化输出格式，适用于任意静态与时序下游任务。代码、简易界面及可选集成的Python包已开源：https://github.com/healthylaife/MIMIC-IV-Data-Pipeline。
原文摘要 · Abstract (English)
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.
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