Department of Critical Care Medicine, The First People's Hospital of Ziyang, Ziyang 641300, Sichuan, China.
School of Science, Shanghai Institute of Technology, Shanghai 201418, China.
J Healthc Eng. 2021 Oct 25;2021:1713363. doi: 10.1155/2021/1713363. eCollection 2021.
This study was to preview the risk of 30-day mortality in sepsis patients using sentiment analysis. The clinical data of patients and nursing notes were collected from the Medical Information Mart for Intensive Care (MIMIC-III) database. The factors influencing 30-day mortality were analyzed using the Cox regression model. And, the prognostic index (PI) was estimated. The receiver operating characteristic (ROC) curve was used to determine the PI cut-off point and assess the prediction ability of the model. In total, 1844 of 3560 patients were eligible for the study, with a 30-day mortality of 37.58%. Multivariate Cox analysis showed that sentiment polarity scores, sentiment subjectivity scores, simplified acute physiology score (SAPS)-II, age, and intensive care unit (ICU) types were all associated with the risk of 30-day mortality ( < 0.05). In the preview of 30-day mortality, the area under the curve (AUC) of ROC was 0.78 (95%CI: 0.74-0.81, < 0.001) when the cut-off point of PI was 0.467. The documented notes from nurses were described for the first time. Sentiment scores measured in nursing notes are associated with the risk of 30-day mortality in sepsis patients and may improve the preview of 30-day mortality.
本研究旨在通过情感分析预测脓毒症患者 30 天死亡率的风险。从医疗信息汇集中采集患者的临床数据和护理记录（MIMIC-III 数据库）。采用 Cox 回归模型分析影响 30 天死亡率的因素，并估计预后指数（PI）。使用接收者操作特征（ROC）曲线确定 PI 截断点，并评估模型的预测能力。共纳入 3560 例患者中的 1844 例，30 天死亡率为 37.58%。多变量 Cox 分析显示，情感极性评分、情感主观性评分、简化急性生理学评分（SAPS）-II、年龄和重症监护病房（ICU）类型均与 30 天死亡率的风险相关（<0.05）。在预测 30 天死亡率方面，当 PI 的截断点为 0.467 时，ROC 曲线下面积（AUC）为 0.78（95%CI：0.74-0.81，<0.001）。护士记录的文档笔记首次被描述。护理记录中测量的情感评分与脓毒症患者 30 天死亡率的风险相关，可能提高 30 天死亡率的预测能力。
