title: Using Active Learning to Identify Health Information Technology Related Patient Safety Events.
abstract: The widespread adoption of health information technology (HIT) has led to new patient safety hazards that are often difficult to identify. Patient safety event reports, which are self-reported descriptions of safety hazards, provide one view of potential HIT-related safety events. However, identifying HIT-related reports can be challenging as they are often categorized under other more predominate clinical categories. This challenge of identifying HIT-related reports is exacerbated by the increasing number and complexity of reports which pose challenges to human annotators that must manually review reports. In this paper, we apply active learning techniques to support classification of patient safety event reports as HIT-related. We evaluated different strategies and demonstrated a 30% increase in average precision of a confirmatory sampling strategy over a baseline no active learning approach after 10 learning iterations.
authors.affiliation: Allan Fong, National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA, 202-244-9807, Email: allan.fong@medicalhfe.org.
authors.forename: Allan
authors.initials: A
authors.lastname: Fong
authors.forename: Jessica L
authors.initials: JL
authors.lastname: Howe
authors.forename: Katharine T
authors.initials: KT
authors.lastname: Adams
authors.forename: Raj M
authors.initials: RM
authors.lastname: Ratwani
mesh_terms: D006801:Humans; D008490:Medical Informatics; D061214:Patient Safety; D000069553:Supervised Machine Learning; D035501:Uncertainty
keywords: Patient safety; active learning; health information technology; human-in-the-loop; machine learning; patient safety event reports