title: Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting.
abstract: Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.
authors: Stockholm University, Sweden.
authors: Claudia
authors: C
authors: Ehrentraut
authors: KTH Royal Institute of Technology, Sweden.
authors: Markus
authors: M
authors: Ekholm
authors: Stockholm University, Sweden.
authors: Hideyuki
authors: H
authors: Tanushi
authors: University of Helsinki, Finland.
authors: Jörg
authors: J
authors: Tiedemann
authors: Stockholm University, Sweden.
authors: Hercules
authors: H
authors: Dalianis
mesh_terms: D000078332:Data Analysis; D057286:Electronic Health Records / Q000706:statistics & numerical data; D006801:Humans; D007049:Iatrogenic Disease; D007239:Infections / Q000145:classification / Q000175:diagnosis* / Q000209:etiology; D000069550:Machine Learning / Q000592:standards* / Q000706:statistics & numerical data; D008403:Mass Screening / Q000379:methods / Q000592:standards; D060388:Support Vector Machine / Q000592:standards*
keywords: clinical decision-making; databases and data mining; ehealth; electronic health records; secondary care