Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data Article Swipe
Andres Quintero
,
Javier Lopez-Molina
,
Merina Su
,
Patrick Long
,
Nicola Boulter
,
C. Weber
,
Ralica Dimitrova
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1136/bmjhci-2024-101282
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1136/bmjhci-2024-101282
Objectives To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning. Methods This cohort study used anonymised, all-payer medical and prescription US claim data from October 2015 to May 2022. First, gradient-boosted decision trees were trained to predict AE in 4 132 973 patients with asthma, of whom 86 735 experienced AE. This model was applied to a holdout set of 86 434 patients with asthma with AE to derive SHapley Additive exPlanations (SHAP) values. SHAP values were then subjected to non-linear dimensionality reduction and density-based clustering to identify distinct subgroups among these patients. These subgroups were described using key clinical and demographic characteristics. Results Clustering identified five distinct subgroups of patients with asthma with AE, broadly differentiated by histories of acute care encounters, healthcare utilisation, AE treatments, coded asthma severity, specialist encounters, first-hand tobacco exposure, mood disorders and patient demographics. Notably, there was considerable between-cluster variability in the predicted likelihood of AE, with some subgroups comprised of patients who posed a challenge for the predictive model and would have been missed with predictive modelling alone. Discussion By identifying distinct subgroups among patients with asthma experiencing AE, this study highlights the heterogeneity within this population and emphasises the need for more personalised management of AE. Conclusion Applying predictive modelling and clustering to real-world data can help identify discrete phenotypes of patients and offer an important source of information for developing risk assessment and mitigation efforts.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/bmjhci-2024-101282
- https://informatics.bmj.com/content/32/1/e101282.full.pdf
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412090595
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412090595Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1136/bmjhci-2024-101282Digital Object Identifier
- Title
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Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-07-01Full publication date if available
- Authors
-
Andres Quintero, Javier Lopez-Molina, Merina Su, Patrick Long, Nicola Boulter, C. Weber, Ralica DimitrovaList of authors in order
- Landing page
-
https://doi.org/10.1136/bmjhci-2024-101282Publisher landing page
- PDF URL
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https://informatics.bmj.com/content/32/1/e101282.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://informatics.bmj.com/content/32/1/e101282.full.pdfDirect OA link when available
- Concepts
-
Asthma, Cluster analysis, Medicine, Medical prescription, Cohort, Population, Machine learning, Artificial intelligence, Internal medicine, Computer science, Environmental health, PharmacologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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