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

Machine Learning (1501) Decision Support for Health Professionals (1210) Secondary Use of Clinical Data for Research and Surveillance (375) Gastroenterology and Hepatology (72)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36997, first published .
Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

Authors of this article:

Yonghui Wu1 Author Orcid Image ;   Xi Yang1 Author Orcid Image ;   Heather L Morris2 Author Orcid Image ;   Matthew J Gurka1 Author Orcid Image ;   Elizabeth A Shenkman1 Author Orcid Image ;   Kenneth Cusi3 Author Orcid Image ;   Fernando Bril4 Author Orcid Image ;   William T Donahoo3 Author Orcid Image
Article Authors Cited by (8) Tweetations (4) Metrics

Yonghui Wu   1 * , PhD ;   Xi Yang   1 * , PhD ;   Heather L Morris   2 , PhD ;   Matthew J Gurka   1 , PhD ;   Elizabeth A Shenkman   1 , PhD ;   Kenneth Cusi   3 , MD ;   Fernando Bril   4 , MD ;   William T Donahoo   3 , MD

1 Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States

2 Target RWE Health Evidence Solutions, Durham, NC, United States

3 Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, United States

4 Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States

*these authors contributed equally

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