Disaggregating asthma: big Investigation vs. big data

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Title: Disaggregating asthma: big Investigation vs. big data
Authors: Belgrave, D
Henderson, J
Simpson, A
Buchan, I
Bishop, C
Custovic, A
Item Type: Journal Article
Abstract: We are facing a major challenge in bridging the gap between identifying subtypes of asthma, to understanding causal mechanisms, and translating this knowledge into personalized prevention and management strategies. In recent years, "big data" has been sold as a panacea for generating hypotheses and driving new frontiers of healthcare; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of healthcare data and computational tools for data analysis is that the process of data mining may become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data-driven and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness 'bigger' healthcare data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts and epidemiologists work together to understand the heterogeneity of asthma.
Issue Date: 18-Nov-2016
Date of Acceptance: 9-Nov-2016
ISSN: 1097-6825
Publisher: Elsevier
Start Page: 400
End Page: 407
Journal / Book Title: Journal of Allergy and Clinical Immunology
Volume: 139
Issue: 2
Copyright Statement: © 2016 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. All rights reserved. This is an open access article under the CC BY license (
Sponsor/Funder: Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Funder's Grant Number: MR/M015181/1
Keywords: Asthma
big data
birth cohorts
machine learning
Big Data
Machine Learning
1107 Immunology
Publication Status: Published
Conference Place: United States
Appears in Collections:Department of Medicine
Faculty of Medicine

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