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Publication Detail
Causality in digital medicine
  • Publication Type:
    Journal article
  • Authors:
    Glocker B, Musolesi M, Richens J, Uhler C
  • Publisher:
    NATURE PORTFOLIO
  • Publication date:
    15/09/2021
  • Journal:
    Nature Communications
  • Volume:
    12
  • Issue:
    1
  • Article number:
    5471
  • Medium:
    Electronic
  • Status:
    Published
  • Country:
    England
  • Print ISSN:
    2041-1723
  • PII:
    10.1038/s41467-021-25743-9
  • Language:
    English
  • Notes:
    This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Abstract
Ben Glocker (an expert in machine learning for medical imaging, Imperial College London), Mirco Musolesi (a data science and digital health expert, University College London), Jonathan Richens (an expert in diagnostic machine learning models, Babylon Health) and Caroline Uhler (a computational biology expert, MIT) talked to Nature Communications about their research interests in causality inference and how this can provide a robust framework for digital medicine studies and their implementation, across different fields of application.
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