文献类型：会议论文

英文题名：Data-driven Discovery of a Sepsis Patients Severity Prediction in the ICU via Pre-training BiLSTM Networks

作者：Li, Qing[1];Huang, L. Frank[2];Zhong, Jiang[1];Li, Lili[3];Li, Qi[4];Hu, Junhao[1]

机构：[1]Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China;[2]Cincinnati Childrens Hosp Med Ctr, Brain Tumor Ctr, Div Expt Hematol & Canc Biol, Cincinnati, OH 45229 USA;[3]Chongqing Univ, Sch Civil Engn, Chongqing, Peoples R China;[4]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing, Zhejiang, Peoples R China

会议论文集：IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

会议日期：NOV 18-21, 2019

会议地点：San Diego, CA

语种：英文

外文关键词：Deep Learning; Sepsis; Intensive Care Units

外文摘要：Sepsis is the third-highest mortality disease in intensive care units(ICU) and expensive treatment costs, but the best treatment strategy remains uncertain. In this paper, we proposed a pre-training bidirectional LSTM Networks to predict the Sepsis severity of patients in ICU. Most previous models for severity prediction rely on the multi-task recurrent neural networks. In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information of organ systems that may be the most crucial features for severity prediction. To address these issues, we propose an end-to-end recurrent neural model which incorporates simultaneously analyses different organ systems and intuitively reflect the condition of the patients in a timely fashion. Specifically, we apply a pre-training technique in our model to combines it with labeled data via multi-task learning. Experimental results on the real-world clinical dataset (MIMIC-III), one of the most popular sepsis severity prediction tasks, demonstrate that our model outperforms existing state-of-the-art models.