Detecting hospital-acquired infections : A document classification approach using support vector machines and gradient tree boosting

• 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 93.7percent recall, 79.7percent precision and 85.7percent 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Infektionsmedicin (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Infectious Medicine (hsv//eng)

Keyword

clinical decision-making

databases and data mining

ehealth

electronic health records

secondary care

Computer and Systems Sciences

data- och systemvetenskap

Publication and Content Type

ref (subject category)

art (subject category)