Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely...
Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information
About this item
Full title
Author / Creator
McTeer, Matthew , Applegate, Douglas , Mesenbrink, Peter , Ratziu, Vlad , Schattenberg, Jörn M. , Bugianesi, Elisabetta , Geier, Andreas , Romero Gomez, Manuel , Dufour, Jean-Francois , Ekstedt, Mattias , Francque, Sven , Yki-Jarvinen, Hannele , Allison, Michael , Valenti, Luca , Miele, Luca , Pavlides, Michael , Cobbold, Jeremy , Papatheodoridis, Georgios , Holleboom, Adriaan G. , Tiniakos, Dina , Brass, Clifford , Anstee, Quentin M. , Missier, Paolo , on behalf of the LITMUS Consortium investigators and LITMUS Consortium investigators
Publisher
United States: Public Library of Science
Journal title
Language
English
Formats
Publication information
Publisher
United States: Public Library of Science
Subjects
More information
Scope and Contents
Contents
Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints.
Using the LITM...
Alternative Titles
Full title
Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information
Authors, Artists and Contributors
Author / Creator
Applegate, Douglas
Mesenbrink, Peter
Ratziu, Vlad
Schattenberg, Jörn M.
Bugianesi, Elisabetta
Geier, Andreas
Romero Gomez, Manuel
Dufour, Jean-Francois
Ekstedt, Mattias
Francque, Sven
Yki-Jarvinen, Hannele
Allison, Michael
Valenti, Luca
Miele, Luca
Pavlides, Michael
Cobbold, Jeremy
Papatheodoridis, Georgios
Holleboom, Adriaan G.
Tiniakos, Dina
Brass, Clifford
Anstee, Quentin M.
Missier, Paolo
on behalf of the LITMUS Consortium investigators
LITMUS Consortium investigators
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_67ecf06493dd4d75bdee07628520f62d
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_67ecf06493dd4d75bdee07628520f62d
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0299487
