Pressure Ulcer Injury in Unstructured Clinical Notes: Detection and Interpretation
Pressure Ulcer Injury in Unstructured Clinical Notes: Detection and Interpretation
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发表时间：
2020
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通讯作者：
Mani Sotoodeh;Zelalem Gero;Wenhui Zhang;L. Roy;Simpson;V. Hertzberg;Joyce Ho
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作者：
Mani Sotoodeh;Zelalem Gero;Wenhui Zhang;L. Roy;Simpson;V. Hertzberg;Joyce Ho
医院获得性压疮损伤（PUI）是一项主要的护理质量指标，反映了医院内的护理质量。先前的研究已使用 Braden 量表和电子健康记录中的结构化数据来检测/预测 PUI，但尚未使用信息丰富的非结构化临床记录。我们建议使用一种应用于非结构化临床记录的新型否定检测算法来进行自动 PUI 检测。我们的检测框架是按需的，需要最低的成本。在应用于 MIMIC-III 数据集时，与没有否定检测的文本特征相比，使用逻辑回归、随机森林和神经网络进行评估时，使用我们的算法生成的文本特征改善了 PUI 检测。探索性分析揭示了 PUI 的关键分类器特征和主要临床属性之间的大量重叠，从而增加了我们解决方案的可解释性。我们的方法还可以通过自动检测大多数病例来显着减少护士的评估，只留下最不确定的病例进行护理评估。
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.