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J Allergy Clin Immunol. 2017 Feb;139(2):400-407. doi: 10.1016/j.jaci.2016.11.003. Epub 2016 Nov 18.

Disaggregating asthma: Big investigation versus big data.

Author information

1
Department of Paediatrics, Imperial College, London, United Kingdom.
2
School of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, Bristol, United Kingdom.
3
Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
4
Health Informatics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
5
Microsoft Research, Cambridge, United Kingdom.
6
Department of Paediatrics, Imperial College, London, United Kingdom. Electronic address: a.custovic@imperial.ac.uk.

Abstract

We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand 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 health care; 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 health care data and computational tools for data analysis is that the process of data mining can 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- 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 health care 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.

KEYWORDS:

Asthma; big data; birth cohorts; endotypes; machine learning

PMID:
27871876
PMCID:
PMC5292995
DOI:
10.1016/j.jaci.2016.11.003
[Indexed for MEDLINE]
Free PMC Article

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