Consortium RECENTRE @Biomedical Engineering Conference
At 4TU RECENTRE Research Program, we focus on developing personalized healthcare and smart monitoring solutions, facilitating the transition from clinical to home-based care through early detection and tailored interventions. Within Work Package 2 (WP2), our objective is to develop dynamic predictive models for identifying complications related to cancer, obesity, or their respective treatments. By leveraging time-dependent patient data, we aim to enhance early detection, facilitate timely interventions, and improve patient outcomes. The WP2 team includes Fatime Oumar Djibrillah, Arlene John and Maurice van Keulen.
We are pleased to share that our research abstracts were presented at BME2025, contributing to the advancement of predictive healthcare:
- Systematic Review on Dynamic Predictive Models
An overview of dynamic predictive models designed to predict side effects following cancer and its treatments, demonstrating how data collected over time can enhance clinical decision-making. This work explores existing methodologies and their potential applications, contributing to the growing field of time-dependent predictive modeling in healthcare.
- Quantification of Urgent Indicators for Early Detection of Complications after Colorectal Resection
A proof-of-principle study exploring how heart rate and respiratory rate data collected over time- within the home environment after colorectal resection surgeries- can support the early detection of postoperative complications. This work was conducted in collaboration with Medisch Spectrum Twente (MST).
A huge thank you to our collaborators Ilse Waanders, Daan Lips, Harry Vaassen, Rebecca Schipper, Agnes A.M. Berendsen, Tina Nane, Annemieke Witteveen, for their invaluable contributions.
