Semantically Steered Clinical Decision Support Systems

Clinical Decision Support Systems (CDSS) are currently a hot topic of research, offering the possibility of enhanced health care services and optimized management of resources. This doctoral dissertation provides an innovative architecture for semantically steered CDSS. We propose the use of domain modeling paradigms for enhancing classical CDSS with a
Knowledge Engineering approach, coining the term S-CDSS. Our work focuses on the practical aspects of decision-making, commonly present in daily clinical practice. This Thesis contains contributions, such as (i) the use of pre-cached Knowledge, (ii) the generation of new architectures for clinical decision-making, and (iii) the Knowledge persistence during
the clinical life cycle. Fundamental to our work is the pioneering use of the modeling and re-use of physicians’ experience that leads towards a repository of the decisions performed. We have implemented our approach in two application domains within industrial projects developed in real world clinical environments: the early diagnosis of Alzheimer’s Disease, and the diagnosis, treatment and follow-up of Breast Cancer.