  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.
