Automatic detection of patients with invasive fungal disease from free-text computed tomography (CT) scans

Abstract

Background: Invasive fungal diseases (IFDs) are associated with considerable health and economic costs. Surveillance of the more diagnostically challenging invasive fungal diseases, specifically of the sino-pulmonary system, is not feasible for many hospitals because case finding is a costly and labour intensive exercise. We developed text classifiers for detecting such IFDs from free-text radiology (CT) reports, using machine-learning techniques. Method: We obtained free-text reports of CT scans performed over a specific hospitalisation period (2003-2011), for 264 IFD and 289 control patients from three tertiary hospitals. We analysed IFD evidence at patient, report, and sentence levels. Th..