Open Collections

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. Synthetic training dataset and data dictionary for the synthetic dataset containing 138 variables.

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