SICdb_MEDS ETL

The SICdb dataset offers insights into over 27 thousand intensive care admissions, including therapies and data on preceding surgeries. Data were collected between 2013 and 2021 from four different intensive care units at the University Hospital Salzburg, having more than 3 thousand intensive care admissions per year on 41 beds. The dataset is deidentified and contains, amongst others, case information, vital signs, laboratory results and medication data. SICdb provides both aggregated once-per-hour and highly granular once-per-minute data, making it suitable for computational and machine learning-based research. (source: https://www.sicdb.com/Documentation/Main_Page)

Usage

pip install SICdb_MEDS # you can do this locally or via PyPI
# Download your data or set download credentials
MEDS_extract-SICdb root_output_dir=$ROOT_OUTPUT_DIR

# or, if you have the data already downloaded
MEDS_extract-SICdb root_output_dir=$ROOT_OUTPUT_DIR do_download=False

# or, if you want enable waveform extraction and processing (takes significantly longer and up to 100GB of RAM)
MEDS_extract-SICdb root_output_dir=$ROOT_OUTPUT_DIR do_process_waveform=True

MEDS-transforms settings

If you want to convert a large dataset, you can use parallelization with MEDS-transforms (the MEDS-transformation step that takes the longest).

Using local parallelization with the hydra-joblib-launcher package, you can set the number of workers:

pip install hydra-joblib-launcher --upgrade

Then, you can set the number of workers as environment variable:

export N_WORKERS=8

Moreover, you can set the number of subjects per shard to balance the parallelization overhead based on how many subjects you have in your dataset:

export N_SUBJECTS_PER_SHARD=100000

Citation

If you use this dataset, please cite the original publication below and the ETL (see cite this repository):

@article{rodemundHarnessingBigData2024,
title = {Harnessing {Big} {Data} in {Critical} {Care}: {Exploring} a new {European} {Dataset}},
volume = {11},
copyright = {2024 The Author(s)},
issn = {2052-4463},
shorttitle = {Harnessing {Big} {Data} in {Critical} {Care}},
url = {https://www.nature.com/articles/s41597-024-03164-9},
doi = {10.1038/s41597-024-03164-9},
language = {en},
number = {1},
urldate = {2024-04-04},
journal = {Scientific Data},
author = {Rodemund, Niklas and Wernly, Bernhard and Jung, Christian and Cozowicz, Crispiana and Koköfer, Andreas},
month = mar,
year = {2024},
note = {Publisher: Nature Publishing Group},
keywords = {Clinical trial design, Experimental models of disease},
pages = {320},
}