Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic

作者：

Highlights：

• The study explicates the patient population that is at increased risk of contracting a healthcare-associated infection.

• Fuzzy sets and rules were used to model linguistic concepts, improving system understandability for clinical professionals.

• For higher-level clinical concepts, there are many borderline cases where data are neither pathological nor fully normal.

摘要

•The study explicates the patient population that is at increased risk of contracting a healthcare-associated infection.•Fuzzy sets and rules were used to model linguistic concepts, improving system understandability for clinical professionals.•For higher-level clinical concepts, there are many borderline cases where data are neither pathological nor fully normal.

论文关键词：Automated monitoring and surveillance systems,Infection control,Fuzzy logic,Intensive care units,Healthcare-associated infections

论文评审过程：Received 3 February 2016, Revised 27 April 2016, Accepted 27 April 2016, Available online 28 April 2016, Version of Record 25 May 2016.

论文官网地址：https://doi.org/10.1016/j.artmed.2016.04.005