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Data Challenges: 2024 Pediatric Sepsis Challenge Nguyen, Vuong; Huxford, Charly; Rafiei, Alireza; Wiens, Matthew; Ansermino, J Mark; Kissoon, Niranjan; Kamaleswaran, Rishikesan

Description

<br /><strong>Objective(s):</strong> The 2024 Pediatric Sepsis Data Challenge provides an opportunity to address the lack of appropriate mortality prediction models for LMICs. For this challenge, we are asking participants to develop a working, open-source algorithm to predict in-hospital mortality and length of stay using only the provided synthetic dataset.

The original data used to generate the real-world data (RWD) informed synthetic training set available to participants was obtained from a prospective, multisite, observational cohort study of children with suspected sepsis aged 6 months to 60 months at the time of admission to hospitals in Uganda. For this challenge, we have created a RWD-informed synthetically generated training data set to reduce the risk of re-identification in this highly vulnerable population. The synthetic training set was generated from a random subset of the original data (full dataset A) of 2686 records (70% of the total dataset - training dataset B). All challenge solutions will be evaluated against the remaining 1235 records (30% of the total dataset - test dataset C).
<br /><strong>Data Description:</strong> Report describing the comparison of univariate and bivariate distributions between the Synthetic Dataset and Test Dataset C. Additionally, a report showing the maximum mean discrepancy (MMD) and Kullback–Leibler (KL) divergence statistics. Data dictionary for the synthetic training dataset containing 148 variables.; <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Item Metadata

Title
Data Challenges: 2024 Pediatric Sepsis Challenge
Creator
Nguyen, Vuong; Huxford, Charly; Rafiei, Alireza; Wiens, Matthew; Ansermino, J Mark; Kissoon, Niranjan; Kamaleswaran, Rishikesan
Contributor
Huxford, Charly
Date Issued
2023-06-21
Description

<br /><strong>Objective(s):</strong> The 2024 Pediatric Sepsis Data Challenge provides an opportunity to address the lack of appropriate mortality prediction models for LMICs. For this challenge, we are asking participants to develop a working, open-source algorithm to predict in-hospital mortality and length of stay using only the provided synthetic dataset.

The original data used to generate the real-world data (RWD) informed synthetic training set available to participants was obtained from a prospective, multisite, observational cohort study of children with suspected sepsis aged 6 months to 60 months at the time of admission to hospitals in Uganda. For this challenge, we have created a RWD-informed synthetically generated training data set to reduce the risk of re-identification in this highly vulnerable population. The synthetic training set was generated from a random subset of the original data (full dataset A) of 2686 records (70% of the total dataset - training dataset B). All challenge solutions will be evaluated against the remaining 1235 records (30% of the total dataset - test dataset C).
<br /><strong>Data Description:</strong> Report describing the comparison of univariate and bivariate distributions between the Synthetic Dataset and Test Dataset C. Additionally, a report showing the maximum mean discrepancy (MMD) and Kullback–Leibler (KL) divergence statistics. Data dictionary for the synthetic training dataset containing 148 variables.; <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Subject
Medicine, Health and Life Sciences; Common data elements; Data science; Infection; Pediatric; Infant; Data curation; Data management; Big data; Sepsis; Child; Medical informatics; Algorithms; Data aggregation
Type
Dataset
Language
English
Date Available
2023-06-20
Provider
University of British Columbia Library
License
CC BY-NC-SA 4.0
DOI
10.14288/1.0433718
URI
https://doi.org/10.5683/SP3/TFAV36
Publisher DOI
https://doi.org/10.5683/SP3/TFAV36
Rights URI
http://creativecommons.org/licenses/by-nc-sa/4.0
Aggregated Source Repository
Dataverse

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CC BY-NC-SA 4.0