Classification of disease recurrence using transition likelihoods with expectation-maximization algorithm.

ABSTRACT: When an infectious disease recurs, it may be due to treatment failure or a new infection. Being able to distinguish and classify these two different outcomes is critical in effective disease control. A multi-state model based on Markov processes is a typical approach to estimating the transition probability between the disease states. However, it can perform poorly when the disease state is unknown. This article aims to demonstrate that the transition likelihoods of baseline covariates can distinguish one cause from another with high accuracy in infectious diseases such as malaria. A more general model for disease progression can be constructed to allow for additional disease outcomes. We start from a multinomial logit model to estimate the disease transition probabilities and then utilize

SUBMITTER: Jiang H 

PROVIDER: S-EPMC9489660 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature