Abstract：
Situation awareness (SA) is an important non-technical skill for nurses. Nurses interact directly with patients and review their clinical signs. If we improve nurses\' SA, they will likely detect clinical changes and prevent patient harm. A clinical endeavor that can benefit from improved nurses\' SA is the prevention of Healthcare-Acquired Urinary Tract Infection (HAUTI). Electronic Health Records contain comprehensive nursing assessment data that researchers can use to analyze trends and provide a context-based understanding of the infection risk factors. We conducted a study that involved extracting nursing assessment data and preparing it for supervised learning algorithms and predicting HAUTI. In this paper, we share the methods we used to prepare the data for supervised learning algorithms and present the challenges related to data missingness.
摘要：
情境意识（SA）是护士重要的非技术性技能。护士直接与患者互动并检查他们的临床体征。如果我们改善护士\'SA,他们可能会发现临床变化并防止患者受到伤害。可以从改进的护士中受益的一项临床努力是预防医疗保健获得性尿路感染(HAUTI)。电子健康记录包含全面的护理评估数据，研究人员可以使用这些数据来分析趋势，并提供对感染风险因素的基于上下文的理解。我们进行了一项研究，涉及提取护理评估数据并将其准备用于监督学习算法并预测HAUTI。在本文中,我们分享了我们用来为监督学习算法准备数据的方法，并提出了与数据缺失相关的挑战。
