MIMIC-IV-Ext-MDS-ED: Multimodal Decision Support in the Emergency Department - a Benchmark Dataset for Diagnoses and Deterioration Prediction in Emergency Medicine

Measurable progress in the development of medical decision support systems has
been hindered by a lack of comprehensive datasets. Many available datasets
focus on narrow prediction tasks and do not include a diverse range of data
types, which limits their effectiveness in real-world clinical settings. This
issue is particularly critical in emergency care, where accurate and timely
diagnoses and the ability to predict patient deterioration are essential.

To address these challenges, we present a new dataset derived from MIMIC-IV,
created specifically for benchmarking multimodal decision support systems in
emergency departments. This dataset includes data from the first 1.5 hours
after the patient's arrival, covering demographics, biometrics, vital signs
(including trends), lab results (including trends), and ECG waveforms. It
allows for the evaluation of predictive models across a broad spectrum of
clinical conditions, including both cardiac and non-cardiac conditions (1428
ICD-10 codes), as well as clinical deterioration measures (15 labels covering
6 clinical deterioration conditions, ICU admission at two horizons, and
mortality at 7 different time horizons).

The integration of diverse data types aims to enhance the clinical relevance
and robustness of decision support systems, facilitating more accurate and
timely predictions in acute care scenarios. We release this dataset to
encourage further research and innovation in emergency medicine and to provide
a resource for the reliable benchmarking of multimodal AI models in the field.