Unstructured clinical data such as nursing notes are relatively less explored for building a predictive model for post-discharge mortality despite containing rich information about the health of the patients. Our work examines a simple bag of words (BOW) approach for 7/30/180/365 day mortality prediction of a patient. We have also explored syntactic sentiment dimensions from nursing notes as a predictor of mortality and report the survival analysis results. Our simple BOW model using logistic regression achieved 0.884 AUC for 30-day mortality. We also found out that the polarity may serve as a proxy for patient’s post-discharge survival.

尽管包含有关患者健康的丰富信息，但对于建立出院后死亡率预测模型的非结构化临床数据（例如护理笔记）的探索相对较少。我们的工作检查了一个简单的词袋 (BOW) 方法来预测患者的 7/30/180/365 天死亡率。我们还探索了护理笔记中的句法情感维度作为死亡率的预测指标，并报告了生存分析结果。我们使用逻辑回归的简单 BOW 模型实现了 0.884 AUC 的 30 天死亡率。我们还发现极性可以作为患者出院后存活率的指标。