Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Journal: Obesity surgery
PMID: 39448457

Abstract

PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver fibrosis in patients with severe obesity through noninvasive tests.

Authors

  • Chien-Hung Lu
    Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
  • Weu Wang
    Division of Digestive Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan.
  • Yu-Chuan Jack Li
    Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
  • I-Wei Chang
    Department of Pathology, Taipei Medical University Hospital, Taipei, Taiwan.
  • Chi-Long Chen
    Department of Pathology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Chien-Wei Su
    Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Chun-Chao Chang
    Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC.
  • Wei-Yu Kao
    Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC.

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