Data Challenges: 2024 Pediatric Sepsis Challenge

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Description:


Objective(s): 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).

Data Description: 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.
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Author(s):
Nguyen, Vuong

Nguyen, Vuong

University of British Columbia
, Huxford, Charly

Huxford, Charly

University of British Columbia
, Rafiei, Alireza

Rafiei, Alireza

Emory University
, Wiens, Matthew

Wiens, Matthew

University of British Columbia
, Ansermino, J Mark

Ansermino, J Mark

University of British Columbia
, Kissoon, Niranjan

Kissoon, Niranjan

University of British Columbia
, and Kamaleswaran, Rishikesan

Kamaleswaran, Rishikesan

Emory University
Contributor(s):
Huxford, Charly
Source Repository:
UBC Dataverse
Series:
Pediatric Sepsis Data CoLab // Open Data Resources
Publisher(s):
Borealis
Access:
Public
License:
CC BY-NC-SA 4.0
URL:
https://doi.org/10.5683/SP3/TFAV36
Publication date:
2023-06-21
Subjects:
Medicine, Health and Life Sciences
Keywords:
Algorithms, Infant, Sepsis, Child, Infection, Data science, Data management, Medical informatics, Data curation, Big data, Common data elements, Data aggregation, Pediatric, and LCSH
Identifier:
https://doi.org/10.5683/SP3/TFAV36

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Citation

APA Citation:
Nguyen, V., Huxford, C., Rafiei, A., Wiens, M., Ansermino, J. M., Kissoon, N., & Kamaleswaran, R. (2023). Data Challenges: 2024 Pediatric Sepsis Challenge [Data set]. UBC Dataverse. https://doi.org/10.5683/SP3/TFAV36
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