Hidden Markov Models for sepsis detection in preterm infants
Machine Learning
2019-10-31 v1 Signal Processing
Abstract
We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants. We investigate the use of classical Gaussian mixture model based HMM, and a recently proposed neural network based HMM. To improve the neural network based HMM, we propose a discriminative training approach. Experimental results show the potential of HMMs over logistic regression, support vector machine and extreme learning machine.
Keywords
Cite
@article{arxiv.1910.13904,
title = {Hidden Markov Models for sepsis detection in preterm infants},
author = {Antoine Honore and Dong Liu and David Forsberg and Karen Coste and Eric Herlenius and Saikat Chatterjee and Mikael Skoglund},
journal= {arXiv preprint arXiv:1910.13904},
year = {2019}
}
Comments
Submitted at the 45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, Spain