AI for Health - Preventing Sepsis
Early prediction of the risk of a preterm born baby developing sepsis by extending an existing classification model with more features, exploring more advanced models and developing a new explainable time-series model.
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Preterm infants have higher risk of developing sepsis
Due to possible complications related to the preterm birth (born before 37 week of pregnancy), preterm infants are admitted to an NICU unit. There is a XGBoost model that uses 3 features for the prediction of the risk that an infant will be diagnosed with sepsis during the admission period in the NICU. Currently, about 20% of the infants will develop a sepsis period during their admission which is related to a higher mortality and adverse long-term outcome. What if there was a better, more reliable solution?
Our challenge partner
GOAL: Improving the ML model for sepsis prediction in preterm newborns
During this challenge organized by FruitPunch AI for Health and UMC Utrecht you will be working on improving the existing XGBoost prediction model with more features, exploring advanced models and developing a new model by taking time-series data into account.
UMC Utrecht’s Neonatology department is currently using an XGBoost model to predict sepsis in newborns. Only three variables have been taken into account in the current model: heart frequency, respiration, oxygen saturation. The current model is analyzed using SHAP to make it more explainable, and doctors receive risk scores of the babies every hour which are effectively the model’s confidence scores for classification.
The goal of this project is to diagnose sepsis earlier, such that doctors can get notified and take action, reducing mortality in preterm newborns.
Challenge Info Session
Who are we looking for?
Anyone with an interest in artificial intelligence or machine learning can apply. We expect some experience with programming languages and an interest in machine learning and AI. Some of the techniques you could be using and should spark your interest are, own experience and initiative with prediction is appreciated as well:
- Medical Analytics
- Time series
- Explainable AI
You can join as a contributor (12 hours per week commitment for 2 months), coach (2-4 hours per week, only for experienced ML professionals) and teacher (to give one relevant ML / domain masterclass).
During the challenge we will organize masterclasses on relevant topics like the use of the cloud computation resources Microsoft, IBM & the HPC Lab have made available for us.
Did you know
- 1 in 10 infants born in the United States is born preterm
- Around 15% of preterm infants will develop a late onset sepsis
- The mortality rate in neonatal sepsis was found to be 20%
Application Deadline: 23 January 2022
Challenge Kick-off: 16 February 2022
Midterm Presentations: 31 March 2022
Final Presentations: 24th of June