GOAL: New prediction model validation and improvement

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 Elisabeth-TweeSteden Ziekenhuis (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.

Who are we 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).

The challenge will run from the 16. November 2021  until the 15. February 2022. 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 about 12 hours per week for 3 months. 

‍Did you know

• Today, 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.