Fong Allan, Howe Jessica L, Adams Katharine T, Ratwani Raj M
Allan Fong, National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA, 202-244-9807, Email:
Appl Clin Inform. 2017 Jan 18;8(1):35-46. doi: 10.4338/ACI-2016-09-CR-0148.
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.
健康信息技术（HIT）的广泛应用带来了新的患者安全隐患，这些隐患往往难以识别。患者安全事件报告是对安全隐患的自我报告描述，提供了与HIT相关的潜在安全事件的一种视角。然而，识别与HIT相关的报告可能具有挑战性，因为它们通常被归类在其他更主要的临床类别之下。报告数量的增加和复杂性给必须人工审查报告的人类注释者带来了挑战，这加剧了识别与HIT相关报告的这一难题。在本文中，我们应用主动学习技术来支持将患者安全事件报告分类为与HIT相关。我们评估了不同的策略，并证明在经过10次学习迭代后，验证性抽样策略的平均精度比无主动学习的基线方法提高了30%。
