Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis-Reference-Cited by-同舟云学术

Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis

Published:2022-03 Issue:3 Volume:4 Page:100441
ISSN:2589-5559
Container-title:JHEP Reports
language:en
Short-container-title:JHEP Reports

Author:

Wong Grace Lai-Hung^ORCID,Hui Vicki Wing-Ki,Tan Qingxiong,Xu Jingwen^ORCID,Lee Hye Won^ORCID,Yip Terry Cheuk-Fung^ORCID,Yang Baoyao,Tse Yee-Kit,Yin Chong,Lyu Fei^ORCID,Lai Jimmy Che-To,Lui Grace Chung-Yan^ORCID,Chan Henry Lik-Yuen^ORCID,Yuen Pong-Chi,Wong Vincent Wai-Sun

Funder

Health and Medical Research Fund

Food and Health Bureau

Publisher

Elsevier BV

Subject

Gastroenterology,Hepatology,Immunology and Allergy,Internal Medicine

Reference30 articles.

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2. World Health Organization. Hepatitis C fact sheet. http://www.who.int/en/news-room/fact-sheets/detail/hepatitis-c. Accessed 18 June 2021.

3. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017;Lancet,2018

4. Liver diseases in the Asia-Pacific region: a lancet gastroenterology & hepatology commission;Sarin;Lancet Gastroenterol Hepatol,2020

5. Global hepatitis report;World Health Organization,2017

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