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Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review
Author(s) -
Spann Ashley,
Yasodhara Angeline,
Kang Justin,
Watt Kymberly,
Wang Bo,
Goldenberg Anna,
Bhat Mamatha
Publication year - 2020
Publication title -
hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.488
H-Index - 361
eISSN - 1527-3350
pISSN - 0270-9139
DOI - 10.1002/hep.31103
Subject(s) - hepatology , liver transplantation , medicine , transplantation , liver disease , disease , artificial intelligence , computer science , machine learning , intensive care medicine , medical physics
Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.