Laboratory parameter‐based machine learning model for excluding non‐alcoholic fatty liver disease (NAFLD) in the general population

• Published in: Alimentary pharmacology & therapeutics Vol. 46; no. 4; pp. 447 - 456
• Main Authors: Yip, T. C.‐F., Ma, A. J., Wong, V. W.‐S., Tse, Y.‐K., Chan, H. L.‐Y., Yuen, P.‐C., Wong, G. L.‐H.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.08.2017
• Subjects: Adult; Alanine; Alanine transaminase; Alanine Transaminase - blood; Anthropometry; Artificial intelligence; Cholesterol; Cross-Sectional Studies; Electronic medical records; Fatty liver; Female; Hemoglobin; Hepatocellular carcinoma; Humans; Laboratories; Learning algorithms; Lipoproteins, HDL - blood; Liver diseases; Logistic Models; Machine Learning; Magnetic resonance spectroscopy; Male; Middle Aged; Non-alcoholic Fatty Liver Disease - diagnosis; Population; Population studies; Regression analysis; ROC Curve; Sensitivity and Specificity; Spectroscopy; Triglycerides - blood

Summary Background Non‐alcoholic fatty liver disease (NAFLD) affects 20%‐40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large‐scale epidemiological studies, existing NAFLD scores often req...