2 Works

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Naeimeh Atabaki-Pasdar, Mattias Ohlsson, Ana Viñuela, Francesca Frau, Hugo Pomares-Millan, Mark Haid, Angus G. Jones, E. Louise Thomas, Robert W. Koivula, Azra Kurbasic, Pascal M. Mutie, Hugo Fitipaldi, Juan Fernandez, Adem Y. Dawed, Giuseppe N. Giordano, Ian M. Forgie, Timothy J. McDonald, Femke Rutters, Henna Cederberg, Elizaveta Chabanova, Matilda Dale, Federico De Masi, Cecilia Engel Thomas, Kristine H. Allin, Tue H. Hansen … & Paul W. Franks
Funder: Henning och Johan Throne-Holsts

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

Naeimeh Atabaki-Pasdar, Mattias Ohlsson, Ana Viñuela, Francesca Frau, Hugo Pomares-Millan, Mark Haid, Angus G Jones, E Louise Thomas, Robert W Koivula, Azra Kurbasic, Pascal M Mutie, Hugo Fitipaldi, Juan Fernandez, Adem Y Dawed, Giuseppe N Giordano, Ian M Forgie, Timothy J McDonald, Femke Rutters, Henna Cederberg, Elizaveta Chabanova, Matilda Dale, Federico De Masi, Cecilia Engel Thomas, Kristine H Allin, Tue H Hansen … & Paul W Franks
ABSTRACT Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in type 2 diabetes (T2D) and beyond. Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and ultimately hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and Findings: We utilized the baseline data from the IMI DIRECT, a multicenter prospective cohort study...

Registration Year

  • 2020
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