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

Abstract EN

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.