A case-based reasoning system for aiding detection and classification of nosocomial infections

• Automatic surveillance of healthcare-associated infections.
• Diagnostic decision support system aiding monitoring and control.
• Case-based reasoning system for classifying nosocomial infections.
• Static rule-based knowledge representation and dynamic induction process.
• Natural language processing for physician narratives and nurses' comments.

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 diagnosis 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 understandable explanations about the system's decisions. Results obtained from a real deployment in a public hospital belonging to the Spanish National Health System trained with 2569 samples belonging to 1800 patients during more than 10 consecutive months recognize the usefulness of the system.

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