The study aims to optimize monitoring techniques to accurately assess cardiovascular, respiratory, and neurological functions during the perioperative phase. A critical analysis will be conducted on clinically used measurement techniques, whose accuracy and limitations have scarcely been mapped out until now. Additionally, new algorithms are being developed to extend the limited applicability of measurement methods for estimating circulating volume.

To arrive at an appropriate treatment, information from various measurement devices needs to be integrated. During surgery, the speed of intervention is crucial, which can lead to errors. We aim to investigate the contribution of semantic AI as a bedside advisory source. Using semantic knowledge graphs, we will develop a model based on existing pathophysiological insights and trained with clinical data within the perioperative data management system.