The context

In many hospitals, a significant portion of overall resources is devoted to identifying and treating patients who are clinically deteriorating. The current model, developed to predict which patients have a high risk of deteriorating, is the Modified Early Warning Score (MEWS), which has been in use for around 20 years in hospitals worldwide. Measurements for this model are taken at least once per day and more often if needed.

The EPD publisher (EPIC) has created a new module that calculates a deterioration index (DI) from data available in the electronic patient files (EPD). The DI model is an ordinal logistic regression model, where the DI is auto-calculated every 15 minutes and outputs a percentage that could be directly interpreted. It is currently not set up to directly initiate action.

The challenge

To find whether this model could perform better than the MEWS we need you! Firstly model validation and adaptation of the DI model will be done to evaluate the output probabilities with outcomes of patients. Secondly the model parameters can be re-estimated to optimize the probability output for the ETZ population or hospital sub-populations. Validating and improving these models will help to better predict the risk of deterioration of patients in the hospital beds.

To achieve this goal, our project will be guided by an expert from the ETZ hospital and FruitPunch AI for Health. The project will kick off in the second week of November and the team will consist of 5 people that will work on the project for ~12 hours/week for 3 months.

Apply for the info session on the platform, where Inge Tamminga of the Elisabeth-TweeSteden Ziekenhuis (ETZ) presents the content of the project.


Apply for this challenge!

Who we’re looking for

Anyone with an interest in learning about a model that can predict patient deterioration can apply. We expect some experience with programming languages and an interest in machine learning. A background experience with linear regression or logistic regression is preferred, but anyone with the right motivation and ‘proof’ of understanding of the concepts discussed in this proposal can sign up!

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 (give one relevant ML / domain masterclass).

During the challenge we will arrange for masterclasses on relevant topics like the use of the cloud computation resources Microsoft, IBM & the HPC Lab have made available for us.

Apply for the challenge HERE!

Challenge format

The challenge will run from the 16th of November until the 15th of February, & you will collaborate with a diverse team from different backgrounds to tackle the challenge.

Some important dates:

Related events & content




Attend the info session!

Apply to join the Challenge

Fill in the Google Form behind this button to become one of the 5 engineers collaborating with ETZ to develop ML models that will help doctors to predict deterioration of patients!


Apply for this challenge!

Background info

🥥 There are many reasons why patients are admitted to the Intensive Care Unit (ICU), ranging from heart attack to stroke, poisoning, pneumonia, and other complications, which can all be scaled under overall deterioration of a patient.

🍉 Currently patients that are at risk of deteriorating have direct daily measurements to predict the standard Modified Early Warning Score (MEWS) model, which has been in use for around 20 years in hospitals worldwide.

🍊 Around 80% of patients have physiological parameters that are outside normal ranges within the 24 hours before intensive care unit (ICU) admission

🥝 Models like MEWS and DI could predict the risk of deterioration earlier using measurements from the electronic patient files, which could lead to better patient care and outcomes. Current models need to be evaluated and developed for specific hospitals, before they can be implemented into the working routine of hospitals.

Partners in this challenge

Learn more