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The potential role of machine learning in modelling advanced chronic liver disease

  • Author Footnotes
    1 Shared first authorship
    Gennaro D'Amico
    Correspondence
    Corresponding author at: Viale Cavarretta 34, 90151 Palermo, Italy.
    Footnotes
    1 Shared first authorship
    Affiliations
    Gatroenterology Unit, Azienda Ospedaliera Ospedali Riuniti Villa Sofia-Cervello, Palermo, Italy

    Gastroenterology Unit, Clinica La Maddalena, Palermo, Italy
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  • Author Footnotes
    1 Shared first authorship
    Agostino Colli
    Footnotes
    1 Shared first authorship
    Affiliations
    Department of Transfusion Medicine and Haematology Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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  • Giuseppe Malizia
    Affiliations
    Gastroenterology Unit, Clinica La Maddalena, Palermo, Italy
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  • Giovanni Casazza
    Affiliations
    Department of Clinical Sciences and Community Health - Laboratory of Medical Statistics, Biometry and Epidemiology "G.A. Maccacaro", Università degli Studi di Milano, Milan, Italy

    Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
    Search for articles by this author
  • Author Footnotes
    1 Shared first authorship
Published:December 30, 2022DOI:https://doi.org/10.1016/j.dld.2022.12.002

      Abstract

      The use of artificial intelligence is rapidly increasing in medicine to support clinical decision making mostly through diagnostic and prediction models. Such models derive from huge databases (big data) including a large variety of health-related individual patient data (input) and the corresponding diagnosis and/or outcome (labels). Various types of algorithms (e.g. neural networks) based on powerful computational ability (machine), allow to detect the relationship between input and labels (learning). More complex algorithms, like recurrent neural network can learn from previous as well as actual input (deep learning) and are used for more complex tasks like imaging analysis and personalized (bespoke) medicine. The prompt availability of big data makes that artificial intelligence can provide rapid answers to questions that would require years of traditional clinical research. It may therefore be a key tool to overcome several major gaps in the model of advanced chronic liver disease, mostly transition from mild to clinically significant portal hypertension, the impact of acute decompensation and the role of further decompensation and treatment efficiency. However, several limitations of artificial intelligence should be overcome before its application in clinical practice. Assessment of the risk of bias, understandability of the black boxes developing the models and models’ validation are the most important areas deserving clarification for artificial intelligence to be widely accepted from physicians and patients.

      Keywords

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