Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: A “non-invasive alternative” to liver biopsy?

Niki Katsiki; Amalia Gastaldelli; Dimitri P. Mikhailidis

doi:https://doi.org/10.1016/j.metabol.2019.154010

doi.org/10.1016/j.metabol.2019.154010

Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: A “non-invasive alternative” to liver biopsy?

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Dimitri P. Mikhailidis

89

Metabolism

Volume: 101, Pages: 154010 - 154010

Published: Dec 1, 2019

Abstract

Non-alcoholic fatty liver disease (NAFLD) is currently the most common chronic liver disease worldwide, affecting 25–30% of the general population [ [1] Fazel Y. Koenig A.B. Sayiner M. Goodman Z.D. Younossi Z.M. Epidemiology and natural history of non-alcoholic fatty liver disease. Metabolism. 2016; 65: 1017-1025https://doi.org/10.1016/j.metabol.2016.01.012 Abstract Full Text Full Text PDF PubMed Scopus (260) Google Scholar ], with its...