Although non-invasive markers of liver fibrosis have come into routine clinical practice, non-invasive markers that correlate with histopathological changes of non-alcoholic steatohepatitis (NASH) are desirable tools to stratify patients for interventions and to monitor disease progression. Machine learning techniques have become popular and been used in various domain to discover the underlying data patterns and develop predictive models. Here, I present a machine learning approach which identified a panel incorporating key biomarkers that would reliably identify NASH among patients with non-alcoholic fatty liver disease (NAFLD).
Sandwiches and refresments provided
The University of Nottingham
E Floor, West Block, Queen's Medical Centre
Nottingham, NG7 2UH
telephone: +44 (0) 115 82 31090
Connect with the University of Nottingham through social media and our blogs.
Campus maps | More contact information | Jobs
Browser does not support script.