Design and Applications of Intelligent Systems in Identifying Future Occurrence of Tuberculosis Infection in Population at Risk

Abstract

Tuberculosis is a treatable but severe disease caused by Mycobacterium tuberculosis (Mtb). Recent statistics by international health organizations estimate the Mtb exposure to have reached over two billion individuals. Delay in disease diagnosis could be fatal, especially to the population at risk, such as individuals with compromised immune systems. Intelligent decision systems (IDS) provide a promising tool to expedite discovery of biomarkers, and to boost their impact on earlier prediction of the likelihood of the disease onset. A novel IDS (iTB) is designed that integrates results from molecular medicine and systems biology of Mtb infection to estimate model parameters for prediction of the dynamics of the gene networks in Mtb-infected laboratory animals. The mouse model identifies a number of genes whose expressions could be significantly altered during the TB activation. Among them, a much smaller number of the most informative genes for prediction of the onset of TB are selected using a modified version of Empirical Risk Minimization as in Vapnik's statistical learning theory. A hybrid intelligent system is designed to take as input the mRNA abundance at a near genome-size from the individual-to-be-tested, measured 3-4 times. The algorithms determine if that individual is at risk of the onset of the disease based on our current analysis of mRNA data, and to predict the values of the biomarkers for a future period (of up to 60 days for mice; this may differ for humans). An early warning sign allows conducting gene expression analysis during the activation which aims to find key genes that are expressed. With rapid advances in low-cost genome-based diagnosis, this IDS architecture provides a promising platform to advance Personalized Health Care based on sequencing the genome and microarray analysis of samples obtained from individuals at risk. The novelty of the design of iTB lies in the integration of the IDS design principles and the solution of the biological problems hand-in-hand, so as to provide an AI framework for biologically better-targeted personalized prevention/treatment for the high-risk groups. The iTB design applies in more generality, and provides the potential for extension of our AI-approach to personalized-medicine to prevent other public health pandemics. © 2011 IFIP International Federation for Information Processing.