Overview
Join the 2024 Pediatric Sepsis Data Challenge to sharpen your data science skills while addressing a critical global health problem! Using a synthetically generated dataset based on real clinical data, you will develop a model to predict in-hospital mortality in children.
Background on the challenge
Sepsis, a severe response to infection leading to organ dysfunction or death, is a leading cause of mortality in children, particularly in developing countries. In 2017, an estimated 48.9 million cases of sepsis were reported globally, with children accounting for over half of these cases. A staggering 85% of these cases occurred in low- and middle-income countries. Many sepsis-related deaths could be prevented with early detection and timely treatment using simple, highly effective interventions like antimicrobials and fluid resuscitation. The 2024 Pediatric Sepsis Data Challenge was created to help address this critical gap in healthcare.
The dataset and challenge objective
The data for the 2024 Pediatric Sepsis Data Challenge comes from a deidentified, curated research dataset of a study called “Smart discharges to improve post-discharge health outcomes in children: A prospective before-after study with staggered implementation”. This study enrolled children under 5 years of age who were admitted with a proven or suspected infectious illness. This dataset is limited to the in-hospital period, and contains clinical, social and demographic data captured at the point of hospital admission. Participants will be provided with a synthetically generated training data set to reduce the risk of patient re-identification. The synthetic training set was generated from a random subset of the original data. Solutions will be evaluated against the remaining original dataset that will be unseen and unavailable to participants.
The challenge is to design and implement a working, open-source algorithm that can predict in-hospital mortality from routinely collected data at the time of presentation to a health facility in Uganda.