Symile-MIMIC: a multimodal clinical dataset of chest X-rays, electrocardiograms, and blood labs from MIMIC-IV

Symile-MIMIC is a multimodal clinical dataset derived from MIMIC-IV and MIMIC-
CXR, consisting of chest X-rays (CXRs), electrocardiograms (ECGs), and blood
laboratory tests. It was developed to evaluate Symile, a contrastive learning
objective designed to handle multiple modalities and enable any model to
generate representations for each modality. The dataset explores whether ECG
and blood work collected at admission are predictive of a CXR taken shortly
thereafter. Symile-MIMIC includes 11,622 admissions, split into training,
validation, and test sets with no patient overlap between splits. Each sample
contains an ECG, a CXR, and up to 50 common blood lab results. This module
provides: (1) the dataset in CSV format, (2) pre-processed tensors of the
dataset, (3) the code to generate the dataset from MIMIC-IV and MIMIC-CXR, and
(4) the best model checkpoint trained on the Symile-MIMIC dataset using the
Symile objective.

Symile-MIMIC是一款源自MIMIC-IV与MIMIC-CXR的多模态临床数据集，涵盖胸部X射线（Chest X-rays，CXRs）、心电图（Electrocardiograms，ECGs）以及血液实验室检测指标。本数据集旨在评估Symile对比学习目标——该目标专为处理多模态数据而设计，可使任意模型能够生成各模态的表征。本数据集聚焦于探究入院时采集的心电图与血液检验结果，是否能够预测随后短期内拍摄的胸部X射线影像。Symile-MIMIC共包含11622例入院病例，划分为训练集、验证集与测试集，且各子集之间无患者重叠。每个样本均包含一份心电图、一份胸部X射线影像，以及最多50项常见血液实验室检测指标。本模块提供如下内容：（1）CSV格式的数据集文件；（2）数据集的预处理张量；（3）从MIMIC-IV与MIMIC-CXR生成该数据集的代码；（4）基于Symile-MIMIC数据集、采用Symile目标函数训练得到的最优模型检查点。

搜集汇总

背景与挑战

背景概述

Symile-MIMIC是一个多模态临床数据集，包含胸部X光片、心电图和血液实验室测试数据，旨在评估Symile对比学习目标。数据集包含11,622次入院记录，分为训练集、验证集和测试集，确保患者不重叠，并探索心电图和血液检测结果对胸部X光片的预测能力。