Machine learning as an artificial intelligence application in management of chronic hepatitis B virus infection

Core Tip

Core Tip: There is substantial evidence indicating that stratification based on the gut microbiome could facilitate personalized interventions aimed at enhancing human health. It became essential to characterize the microbial ecosystems, resulting in a surge of various types of molecular profiling data, including metagenomics, metatranscriptomics, and metabolomics. In the analysis of such data, machine learning algorithms have proven to be effective in identifying crucial molecular signatures, uncovering potential patient stratifications, and especially in creating models that can reliably predict phenotypes. Machine learning may be supervised, unsupervised, semi-supervised or reinforcement type. Using a method for explaining individual classifier decisions for complex microbiota analysis may help in developing personalized treatment.