This task aims to validate and demonstrate the utilisation of the federated infrastructure for the special case of data access to data of paediatric patients.
The additional complexity is in terms of the legal requirements of data protection for under aged data subjects across Member States. Current interpretation of data protection rules often results in data of paediatric patients not being shared at all, stifling innovation. Paradoxically, data sharing for research and innovation in paediatrics is vital, because populations of paediatric patients tend to be too small at individual institutions to enable research and in particular training and evaluation of machine learning models to support clinical practice.
We will focus on sepsis at the neonatal and paediatric ICU, as sepsis remains the most common cause of neonatal (less than 28 days old) and children’s death. We will validate a promising, recently described early detection algorithm for sepsis that combines patient characteristics and bedside monitor data to generate a risk score for sepsis. Without large scale validation, implementing and adopting such an algorithm would not be possible.
This task involves the following steps:
T6.4.1
External validation of the early detection algorithm developed by Van Laere et al. We will assess the functionality of the algorithm in different neonatal populations, external validation is required in multiple datasets from different hospitals throughout Europe.
T6.4.2
Refine the model to local settings. Even if results from external validation appear, it will be useful to assess the performance of the detection models after they have been retrained using all data from all patients as a training dataset. This effort will likely increase generalisability for application in different populations.
T6.4.3
Set-up a continuous federated evaluation and retraining pipeline. After the early sepsis detection models are implemented, continuous evaluation of model performance is necessary to guarantee effectiveness and patient safety.
Use case leader
