PhD defence E. (Eris) van Twist

Below is a brief summary of the dissertation:

Every day, paediatric intensive care units collect large amounts of data from monitors that track a child's health and ogan functions. Although these data have the potential to improve patient care, it remains difficult to turn them into information that clinicians can use in real time.
This thesis focuses on improving the development and evaluation of data-driven algorithms for paediatric critical care. A framework called the data pipeline was used to guide the process from raw patient data to clinically meaningful information, and a new quality assessment tool, DATA-CARE, was developed to evaluate healthcare algorithms. The findings show that high-quality data, transparent methods, and thorough validation are essential for creating reliable algorithms that can be trusted in clinical practice.
By combining methodological research with practical clinical applications, this work helps bridge the gap between algorithm development and implementation, ultimately supporting safer and more effective care for critically ill children.