A Comparative Analysis of HMM and CRF for Early Prediction of Sepsis

Abstract

This study aims to detect and predict sepsis precisely in ICU patients according to the data published for Physionet challenge 2019. Sepsis prediction can help in early intervention and therefore less mortality rate. Hidden Markov Model (HMM) is applied with the independence assumption of features; however, to tackle this problem, Linear-chain conditional random field (CRF) is implemented and the results are compared to HMM. The results show that CRF outperforms HMM in the early prediction of sepsis. The team of the authors, named IMSAT, ranked 50 in the mentioned challenge by gaining a utility score of 0.19.