Disaggregating asthma: big investigation versus big data
Open accessAbstract
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.
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Bayesian and frequentist approaches can be applied to both big and small data in healthcare research.
“Bayesian and frequent approaches make us understand about the ethnology of diseases commonly used in statistical machine learning and can be applied to both big and small data in healthcare research.”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [65]
Different mechanisms may give rise to similar or almost identical observable traits, while the same underlying mechanism may also result in distinct phenotypes in different patients.
“However, it is important to note that different mechanisms may give rise to similar or almost identical observable traits, while the same underlying mechanism may also result in distinct phenotypes in different patients (25).”
From Epidemiology of Asthma in Children and Adults (2019) · cites this paper as [25]
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- Authors
- Belgrave, D., Henderson, J., Simpson, A., Buchan, I., Bishop, C., & Custovic, A.
- Affiliations
- Imperial College London (GB)
- University of Bristol (GB)
- University of Manchester (GB)
- Venue
- Journal of Allergy and Clinical Immunology
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 139
- Issue
- 2
- Pages
- 400-407
- ISSN
- 0091-6749 (ISSN-L)
- PMC
- PMC5292995
- OpenAlex
- W2554463361
- Cited by
- 67
- References
- 86
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 8 · 2020: 10 · 2019: 19 · 2018: 16 · 2017: 1
- FWCI
- 9.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Allergy and Clinical Immunology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- GlaxoSmithKline
- Sanofi
- Microsoft Research
- Regeneron Pharmaceuticals
- Medical Research Council (MR/K002449/1, M015181, MR/M015181/1, MR/K006665/1, MR/K002449/2, MC_PC_13042)
- Topics
- Health Sciences › Medicine › Physiology › Asthma and respiratory diseases
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Keywords
- asthma subtypes, asthma heterogeneity, big data, data mining, hypothesis-driven research, data provenance, external validation, birth cohort data, personalized asthma management, asthma diagnosis
- MeSH terms
- Asthma, Electronic Data Processing, Delivery of Health Care, United Kingdom, Humans, Phenotype, Software, Computational Biology, Interdisciplinary Communication, Translational Research, Biomedical, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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