A Time-Critical Topic Model for Predicting the Survival Time of Sepsis Patients

Wenping Guo,Zhuoming Xu,Xue Li,Shiqing Zhang,Xijian Ye,Xiaoming Zhao

doi:10.1155/2020/8884539

Abstract

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.

Highlights

Predicting the survival time of patients is an active research area for both clinicians and scientists [1,2,3,4,5]
This paper proposes a time-critical topic model (TiCTM) inspired by the latent Dirichlet allocation (LDA) model to predict the survival time of sepsis patients. e proposed TiCTM approach takes into account the time dependency structure between notes, measurement, and survival time of a sepsis patient
We propose a time-critical topic model inspired by LDA to predict the survival time of sepsis patients, as described as follows

Summary

Introduction

Predicting the survival time of patients is an active research area for both clinicians and scientists [1,2,3,4,5]. Sepsis is a disease of life-threatening organ dysfunction caused by a dysregulated host response to infection [6]. Sepsis can rapidly lead to tissue damage, organ failure, and death. Clinicians have made efforts to improve sepsis patient survival time, the mortality rate of sepsis is still very high [9, 10]. Us, accurate prediction of survival time for sepsis patients could help clinicians conduct prevention, provide early warning and effective treatment, and reduce the mortality rate. Predicting the survival time for specific diseases, such as sepsis, is still a challenging problem

Methods

Results

Conclusion