title: Assessing the clinical uses of fuzzy detection results in the automated detection of CVC-related infections: a preliminary report.
abstract: Central venous catheters (CVCs) play an essential role in the care of the critically ill, but their use comes at the risk of infection. By using fuzzy set theory and logic to model clinical linguistic CVC-related infection criteria, clinical detection systems can detect borderline infections where not all infection parameters have been (fully) met, also called fuzzy results. In this paper we analyzed the clinical use of these results. We used a fuzzy-logic-based computerized infection control system for the monitoring of healthcare-associated infections to uncover fuzzy results and periods, after which we classified them, and used these classifications together with knowledge of prior CVC-related infection episodes in temporal association rule mining. As a result, we uncovered several rules which can help with the early detection of re-occurring CVC-related infections.
authors.affiliation: Medical University of Vienna, Austria. jeroen.debruin@meduniwien.ac.at
authors.forename: Jeroen S
authors.initials: JS
authors.lastname: de Bruin
authors.forename: Alexander
authors.initials: A
authors.lastname: Blacky
authors.forename: Klaus-Peter
authors.initials: KP
authors.lastname: Adlassnig
mesh_terms: D055499:Catheter-Related Infections / Q000175:diagnosis* / Q000209:etiology*; D062905:Central Venous Catheters / Q000009:adverse effects*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005060:Europe; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D011159:Population Surveillance / Q000379:methods; D012042:Registries; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity