This paper is in the following e-collection/theme issue:

Machine Learning (1954) Decision Support for Health Professionals (1460) Artificial Intelligence (1985) Clinical Information and Decision Making (1802) Gastroenterology and Hepatology (104)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26398, first published .
Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison

Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison

Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison

Authors of this article:

Cheng-Tse Wu1 Author Orcid Image ;   Ta-Wei Chu2, 3 Author Orcid Image ;   Jyh-Shing Roger Jang1 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. 邹 慧. Research Progress in the Application of Data Mining Techniques for Metabolic-Associated Fatty Liver Disease Risk Prediction. Nursing Science 2025;14(06):956 View
  2. Cubillos G, Perez-Valenzuela J, Aguirre H, Martínez L, Castro L, Mezzano G, Perez C. Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD. Scientific Reports 2025;15(1) View