Bayesian hidden Markov models for delineating the pathology of Alzheimer's disease.

ABSTRACT: Alzheimer's disease is a firmly incurable and progressive disease. The pathology of Alzheimer's disease usually evolves from cognitive normal, to mild cognitive impairment, to Alzheimer's disease. The aim of this paper is to develop a Bayesian hidden Markov model to characterize disease pathology, identify hidden states corresponding to the diagnosed stages of cognitive decline, and examine the dynamic changes of potential risk factors associated with the cognitive normal-mild cognitive impairment-Alzheimer's disease transition. The hidden Markov model framework consists of two major components. The first one is a state-dependent semiparametric regression for delineating the complex associations between clinical outcomes of interest and a set of prognostic biomarkers across neurodegenerati

SUBMITTER: Kang K 

PROVIDER: S-EPMC5984196 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature