A case-based reasoning system for aiding detection and classification of nosocomial infections
Authors: Gómez-Vallejo, H.J.; Uriel-Latorre, B.; Sande-Meijide, M.; Villamarín-Bello, B.; Pavón, R.; Fdez-Riverola, F.; Glez-Peña, D.
Journal: Decision Support Systems (2016)
DOI: 10.1016/j.dss.2016.02.005
Nowadays, it is recognized worldwide that healthcare-associated infections are responsible for an increase in patient morbidity, mortality and higher costs related to prolonged hospital stays. As electronic health data are increasingly available today, there is a unique opportunity to implement real time decision support systems for automating the surveillance of healthcare-associated infections. As a consequence, different electronic surveillance systems have been implemented to date with varying degrees of success. However, there have been few instances in which clinical data and physician narratives with the potential to significantly improve electronic surveillance alternatives have been adopted. In this context, the present work introduces a case-based reasoning system for the automatic surveillance and diagnose of healthcare-associated infections. The developed system makes use of different machine learning techniques in order to (i) automatically extract evidence from different types of data including clinical unstructured documents, (ii) incorporate static a priori knowledge handled by infection preventionists and (iii) dynamically generate new knowledge as well as underst…