The training consisted of a 1 day "kennis-maken" with the available tools and opportunities from control engineering domain to the pharmaceutical engineering domain of application. A simulator of tabletting process has been used a backbone for illustrating the main concepts. 

Title: How digital twins and machine learning resolve data scarcity and poor excitability in rela time decisionj-making in healthcare systems

Abstract: 

Our patient digital twin including digitalization of the operation room or critical care room and human in the loop is a comprehensive platform for establishing new therapies, methodologies and validating protocols of trauma and critical care in emergency care units. Our solutions for Adaptive Multi-drug Infusion Control system for general Anesthesia integrates human expertise with computer optimization to create a successful solution for breakthrough into clinical practice. In presence of scarce data availability and limited versatility in producing excitability nodes for model identification and design model based control strategies, it is challenging to make decisions in real time due to epistemic uncertainty.

A potential complementary tool is Artificial intelligence (AI), as it becomes an integrating unit in all societal-impact applications. In this plenary talk we look at what challenges make AI not directly applicable for certain dynamic processes, and what are its limitations. One of the most (controversial) experience in practice is the fact that data volumes are (very) large, but the information content is limited to offer AI a solution that is also directly actionable. We develop methodologies and algorithms to deal with these limitations in data content/accessibility and allow complementary AI results to combine with traditional methods for the optimal result.

The combined digital twin with AI features is a co-piloting system of computer based optimization with human experience and comes to aid less experienced anesthesiologists and decision-making in presence of less uncertainty, thereby increasing the safety of the proposed drug dosage profiles.

The proposed solution involves a collective expert support to reach optimal dosage for best outcome under worst operating conditions, whether this is contextual (lack of resources) or human-centric (lack of expertise, unavailable patient information).

Title:

Data-Driven surface response model identification during induction and maintenance phases of general anesthesia

This workshop aims to facilitate the transfer of knowledge between engineering results and expertise at the TW08 Ghent University AMICAS ERC team to the Department of Automatic Control at Technical University of Cluj Napoca, Romania, OPTIMDRUG team. 

This workshop aims to facilitate the transfer of knowledge between engineering results and expertise at the TW08 Ghent University team to the 2K12 Anesthesia Department at Ghent University Hospital. 

This workshop aims to facilitate the transfer of knowledge between engineering results and expertise at the TW08 Ghent University team to the 2K12 Anesthesia Department at Ghent University Hospital. 

Chair of the Diversity and Inclusion program at the trademark conference in control. The session organized around theme: Sustainability of gender Dimension in Deep-Tech Domain intends to present challenges and opportunities of career-growth and decision pathways in gender related context. The aspect of sustainability is emphasized as part of the personal development plan.  

Thesis submitted in fulfillment of the requirements for the award of the degree of Doctor in Medical Sciences.

Promoter: Prof Kurt Barbé, Prof Mark De Ridder, Dr Inés Dufait.