WP 2 - University: UT-1
Faculty: Electrical Engineering, Mathematics and Computer Science
Dynamic modelling for Complications after Cancer and Obesity
WP2 focuses on the development of AI-driven dynamic predictive models to detect complications arising from cancer, obesity, and their treatments. The goal is to use continuous patient monitoring data to track clinical changes over time, enabling early identification of health deterioration. These models will provide clinicians with valuable insights to support timely interventions, improve post-treatment care, and enhance patient outcomes through personalized and proactive healthcare solutions.
Status Before WP2 & Current Status
Before WP2 began its activities, the work within the consortium was at the stage of conceptualizing the application of dynamic models for predicting post-treatment complications and identifying relevant data sources. We only had ideas, but didn’t know how to achieve this and which partners to collaborate with.
Since WP2 began its activities (developments in the last 2 years), we have carried out various steps to achieve the goals of the work package as described in the RECENTRE proposal.
- Collaboration & Data Access: Ongoing collaboration with multiple institutions to get access to relevant patient datasets for model development. Currently we have established collaboration with Prinses Maxima Centre for obtaining data on late effects after childhood cancer and with ZiekenhuisGroep Twente for access to their bariatric surgery follow-up database.
- Systematic Review: Conducting a systematic review on dynamic models for predicting complications after cancer or cancer treatment to existing approaches and guide future model development.
- Data collection: Small pilot on collecting vital signs and activity data using the Philips healthdot sensor in a cohort of 30 patients who underwent major abdominal oncologic surgery to monitor for complications. This was carried out in collaboration with Medisch Spectrum Twente.
- Preprocessing & Model Development: Initial work on processing patient data and preliminary development of machine learning models to predict early signs of complications in patients ongoing major abdominal oncological surgery.
- Setting up a new study to monitor symptoms in cancer survivors based on data obtained from wearables (in collaboration with Helen Dowling Institute and WP5).
- Pilot research into how digital phenotyping using smartphones can be used to detect the onset of late-effects in cancer survivors.
- Presentations at various conferences on the work carried out in WP2.