A systematic review of machine learning models for predicting outcomes of stroke with structured data

Wenjuan Wang; Martin Kiik; Niels Peek; Vasa Curcin; Iain J. Marshall; Anthony G. Rudd; Yanzhong Wang; Abdel Douiri; Charles D. Wolfe; Benjamin Bray

doi:10.1371/journal.pone.0234722

Loading metrics

Open Access

Peer-reviewed

Research Article

A systematic review of machine learning models for predicting outcomes of stroke with structured data

Wenjuan Wang ,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: wenjuan.wang@kcl.ac.uk

Affiliation School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom

http://orcid.org/0000-0002-1879-7332

⨯
Martin Kiik,

Roles Data curation, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Medical Education, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
⨯
Niels Peek,

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Validation, Writing – review & editing

Affiliations Division of Informatics, Imaging and Data Science, School of Health Sciences, University of Manchester, Manchester, United Kingdom, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
⨯
Vasa Curcin,

Roles Formal analysis, Funding acquisition, Validation, Writing – review & editing

Affiliations School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom, NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom, NIHR Applied Research Collaboration (ARC) South London, London, United Kingdom
⨯
Iain J. Marshall,

Roles Formal analysis, Methodology, Validation, Writing – review & editing

Affiliation School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
⨯
Anthony G. Rudd,

Roles Formal analysis, Funding acquisition, Validation, Writing – review & editing

Affiliation School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
⨯
Yanzhong Wang,

Roles Formal analysis, Validation, Writing – review & editing

Affiliations School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom, NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom, NIHR Applied Research Collaboration (ARC) South London, London, United Kingdom
⨯
Abdel Douiri,

Roles Formal analysis, Validation, Writing – review & editing

Affiliations School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom, NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom, NIHR Applied Research Collaboration (ARC) South London, London, United Kingdom
⨯
Charles D. Wolfe,

Roles Funding acquisition, Project administration, Validation, Writing – review & editing

Affiliations School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom, NIHR Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom, NIHR Applied Research Collaboration (ARC) South London, London, United Kingdom
⨯
Benjamin Bray

Roles Conceptualization, Formal analysis, Funding acquisition, Project administration, Validation, Writing – review & editing

Affiliation School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
⨯

A systematic review of machine learning models for predicting outcomes of stroke with structured data

Wenjuan Wang,
Martin Kiik,
Niels Peek,
Vasa Curcin,
Iain J. Marshall,
Anthony G. Rudd,
Yanzhong Wang,
Abdel Douiri,
Charles D. Wolfe,
Benjamin Bray

Published: June 12, 2020
https://doi.org/10.1371/journal.pone.0234722

Reader Comments (0)

Post a new comment on this article

Subject Areas
?

For more information about PLOS Subject Areas, click here.
We want your feedback. Do these Subject Areas make sense for this article? Click the target next to the incorrect Subject Area and let us know. Thanks for your help!

Forecasting
Is the Subject Area "Forecasting" applicable to this article?

Thanks for your feedback.
Artificial neural networks
Is the Subject Area "Artificial neural networks" applicable to this article?

Thanks for your feedback.
Machine learning algorithms
Is the Subject Area "Machine learning algorithms" applicable to this article?

Thanks for your feedback.
Statistical data
Is the Subject Area "Statistical data" applicable to this article?

Thanks for your feedback.
Stroke
Is the Subject Area "Stroke" applicable to this article?

Thanks for your feedback.
Machine learning
Is the Subject Area "Machine learning" applicable to this article?

Thanks for your feedback.
Systematic reviews
Is the Subject Area "Systematic reviews" applicable to this article?

Thanks for your feedback.
Algorithms
Is the Subject Area "Algorithms" applicable to this article?

Thanks for your feedback.