Machine learning in asthma research: moving toward a more integrated approach
File(s)
Author(s)
Fontanella, Sara
Cucco, Alex
Custovic, Adnan
Type
Journal Article
Abstract
Introduction: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that ‘data will speak for themselves’, hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterize asthma heterogeneity, very few research findings have been translated into clinically actionable solutions.
Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.
Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of ‘big data’, we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.
Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.
Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of ‘big data’, we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.
Date Issued
2021-03-16
Date Acceptance
2021-09-19
Citation
Expert Review of Respiratory Medicine, 2021, 15 (5), pp.609-621
ISSN
1747-6348
Publisher
Taylor and Francis Group
Start Page
609
End Page
621
Journal / Book Title
Expert Review of Respiratory Medicine
Volume
15
Issue
5
Copyright Statement
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000629464500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Asthma
big data
data analytics
FUTURE
Life Sciences & Biomedicine
LUNG-FUNCTION
machine learning
PATTERNS
PHENOTYPES
PREDICTION
Respiratory System
Science & Technology
statistics
Publication Status
Published
Date Publish Online
2021-02-23