Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions of dollars annually worldwide. Predicting survival time for sepsis patients is a time-critical prediction problem. Considering the useful sequential information for sepsis development, this paper proposes a time-critical topic model (TiCTM) inspired by the latent Dirichlet allocation (LDA) model. The proposed TiCTM approach takes into account the time dependency structure between notes, measurement, and survival time of a sepsis patient. Experimental results on the public MIMIC-III database show that, overall, our method outperforms the conventional LDA and linear regression model in terms of recall, precision, accuracy, and F1-measure. It is also found that our method achieves the best performance by using 5 topics when predicting the probability for 30-day survival time.

中文翻译：

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预测脓毒症患者生存时间的时间关键主题模型

脓毒症是重症监护病房死亡的主要原因，每年给全球医院造成数十亿美元的损失。预测脓毒症患者的生存时间是一个时间紧迫的预测问题。考虑到脓毒症发展的有用序列信息，本文提出了一种受潜在狄利克雷分配（LDA）模型启发的时间关键主题模型（TiCTM）。提议的 TiCTM 方法考虑了脓毒症患者的笔记、测量和生存时间之间的时间依赖性结构。在公共 MIMIC-III 数据库上的实验结果表明，总体而言，我们的方法在召回率、精度、准确度和 F1 度量方面优于传统的 LDA 和线性回归模型。