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Using multiple sentiment dimensions of nursing notes to predict mortality in the intensive care unit

机译：使用护理笔记的多个情感维度来预测重症监护病房的死亡率

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Unstructured clinical data such as nursing notes or discharge summaries are seldom used to predict clinical outcomes, despite containing a lot of information. This study examined several sentiment dimensions of nursing notes for their contributions to 30-day mortality prediction, in the presence of a known predictor of 30-day mortality (SAPS-II). Sentiment dimensions were extracted using a combination of word frequency and machine learning methods. Gender and type of intensive care unit (ICU) were also included as candidate features. The sentiment dimensions are then ranked via a correlation feature selection filter and a recursive feature elimination. SAPS-II was consistently ranked as the best predictor. With a random forest classifier, the predictive performance was significantly improved with sentiment dimensions features (p-value <;0.05) (mean [standard deviation] area under the receiver operating curve with sentiment dimensions: 0.827 [0.011]; without sentiment dimensions: 0.572 [0.010]). Similar improvement was also observed with a logistic regression classifier (p-value <;0.05) (with sentiment dimensions: 0.824 [0.012]; without sentiment dimensions: 0.785 [0.013]). Improvements to mortality prediction is possible by including sentiment analysis.
机译：尽管包含大量信息，但很少使用非结构化的临床数据（例如护理说明或出院摘要）来预测临床结果。这项研究在已知的30天死亡率预测因子（SAPS-II）存在的情况下，检查了护理笔记的多个情感维度对30天死亡率预测的贡献。使用单词频率和机器学习方法的组合来提取情感维度。重症监护病房的性别和类型也包括在内。然后，通过相关特征选择过滤器和递归特征消除对情感维度进行排序。 SAPS-II一直被评为最佳预测指标。使用随机森林分类器，通过情感维度特征（p值<; 0.05）（在情感维度为0.827 [0.011]的接收者操作曲线下的均值[标准偏差]区域;无情感维度为0.572），预测性能得到了显着改善。 [0.010]）。使用逻辑回归分类器（p值<; 0.05）（情感维度：0.824 [0.012];无情感维度：0.785 [0.013]）也观察到了类似的改善。通过包括情绪分析，可以改善死亡率预测。

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