Literature DB >> 34976373

Advances in Predictive Modeling Using Machine Learning in the Field of Hepatology.

Camille A Kezer1, Vijay H Shah2, Douglas A Simonetto2.   

Abstract

Content available: Author Interview and Audio Recording.
© 2021 by the American Association for the Study of Liver Diseases.

Entities:  

Year:  2021        PMID： 34976373      PMCID： PMC8688898          DOI： 10.1002/cld.1148

Source DB:  PubMed          Journal:  Clin Liver Dis (Hoboken)        ISSN： 2046-2484


  10 in total

1.  Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation.

Authors:  Dimitris Bertsimas; Jerry Kung; Nikolaos Trichakis; Yuchen Wang; Ryutaro Hirose; Parsia A Vagefi
Journal:  Am J Transplant       Date:  2018-12-06       Impact factor: 8.086

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

Authors:  T C-F Yip; A J Ma; V W-S Wong; Y-K Tse; H L-Y Chan; P-C Yuen; G L-H Wong
Journal:  Aliment Pharmacol Ther       Date:  2017-06-06       Impact factor: 8.171

3.  Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

Authors:  Lawrence Lau; Yamuna Kankanige; Benjamin Rubinstein; Robert Jones; Christopher Christophi; Vijayaragavan Muralidharan; James Bailey
Journal:  Transplantation       Date:  2017-04       Impact factor: 4.939

Review 4.  Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Authors:  Ashley Spann; Angeline Yasodhara; Justin Kang; Kymberly Watt; Bo Wang; Anna Goldenberg; Mamatha Bhat
Journal:  Hepatology       Date:  2020-03-06       Impact factor: 17.425

5.  Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes of the Disease: A Derivation and Validation Study Using Machine Learning.

Authors:  John E Eaton; Mette Vesterhus; Bryan M McCauley; Elizabeth J Atkinson; Erik M Schlicht; Brian D Juran; Andrea A Gossard; Nicholas F LaRusso; Gregory J Gores; Tom H Karlsen; Konstantinos N Lazaridis
Journal:  Hepatology       Date:  2018-12-28       Impact factor: 17.425

6.  Non-invasive separation of alcoholic and non-alcoholic liver disease with predictive modeling.

Authors:  Jan-Peter Sowa; Özgür Atmaca; Alisan Kahraman; Martin Schlattjan; Marion Lindner; Svenja Sydor; Norbert Scherbaum; Karoline Lackner; Guido Gerken; Dominik Heider; Gavin E Arteel; Yesim Erim; Ali Canbay
Journal:  PLoS One       Date:  2014-07-02       Impact factor: 3.240

7.  Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model.

Authors:  Hyung-Chul Lee; Soo Bin Yoon; Seong-Mi Yang; Won Ho Kim; Ho-Geol Ryu; Chul-Woo Jung; Kyung-Suk Suh; Kook Hyun Lee
Journal:  J Clin Med       Date:  2018-11-08       Impact factor: 4.241

8.  Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

Authors:  Gu-Wei Ji; Fei-Peng Zhu; Qing Xu; Ke Wang; Ming-Yu Wu; Wei-Wei Tang; Xiang-Cheng Li; Xue-Hao Wang
Journal:  EBioMedicine       Date:  2019-11-15       Impact factor: 8.143

Review 9.  Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases.

Authors:  Joseph C Ahn; Alistair Connell; Douglas A Simonetto; Cian Hughes; Vijay H Shah
Journal:  Hepatology       Date:  2021-06       Impact factor: 17.425

10.  Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach.

Authors:  Johannes Thüring; Oliver Rippel; Christoph Haarburger; Dorit Merhof; Philipp Schad; Philipp Bruners; Christiane K Kuhl; Daniel Truhn
Journal:  Eur Radiol Exp       Date:  2020-04-06
  10 in total

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