Causality in digital medicine

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 biolo...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Q&A
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443583/
https://www.ncbi.nlm.nih.gov/pubmed/34526509
http://dx.doi.org/10.1038/s41467-021-25743-9
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collection PubMed
description 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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spelling pubmed-84435832021-10-04 Causality in digital medicine Nat Commun Q&A 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. Nature Publishing Group UK 2021-09-15 /pmc/articles/PMC8443583/ /pubmed/34526509 http://dx.doi.org/10.1038/s41467-021-25743-9 Text en © Springer Nature Limited 2021 https://creativecommons.org/licenses/by/4.0/Open Access 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Q&A
Causality in digital medicine
title Causality in digital medicine
title_full Causality in digital medicine
title_fullStr Causality in digital medicine
title_full_unstemmed Causality in digital medicine
title_short Causality in digital medicine
title_sort causality in digital medicine
topic Q&A
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443583/
https://www.ncbi.nlm.nih.gov/pubmed/34526509
http://dx.doi.org/10.1038/s41467-021-25743-9

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