Department of Computer Science, Emory University, Atlanta, GA, US.
Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, US.
AMIA Annu Symp Proc. 2021 Jan 25;2020:1160-1169. eCollection 2020.
Hospital-acquired pressure ulcer injury (PUI) is a primary nursing quality metric, reflecting the caliber of nursing care within a hospital. Prior studies have used the Braden scale and structured data from the electronic health records to detect/predict PUI while the informative unstructured clinical notes have not been used. We propose automated PUI detection using a novel negation-detection algorithm applied to unstructured clinical notes. Our detection framework is on-demand, requiring minimal cost. In application to the MIMIC-III dataset, the text features produced using our algorithm resulted in improved PUI detection when evaluated using logistic regression, random forests, and neural networks compared to text features without negation detection. Exploratory analysis reveals substantial overlap between key classifier features and leading clinical attributes of PUI, adding interpretability to our solution. Our method could also considerably reduce nurses' evaluations by automatic detection of most cases, leaving only the most uncertain cases for nursing assessment.
医院获得性压力性溃疡损伤（PUI）是一个主要的护理质量指标，反映了医院内护理的水平。先前的研究已经使用布雷登量表和电子健康记录中的结构化数据来检测/预测 PUI，而未使用有信息的非结构化临床记录。我们提出了一种使用新颖的否定检测算法自动检测 PUI 的方法，该算法应用于非结构化临床记录。我们的检测框架是按需的，需要的成本最小。在应用于 MIMIC-III 数据集时，与未进行否定检测的文本特征相比，使用我们的算法生成的文本特征在使用逻辑回归、随机森林和神经网络进行评估时，可提高 PUI 的检测效果。探索性分析表明，关键分类器特征与 PUI 的主要临床属性之间存在大量重叠，为我们的解决方案增加了可解释性。我们的方法还可以通过自动检测大多数病例来大大减少护士的评估，只留下最不确定的病例供护理评估。
