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.