Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data
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.
Authors
- Patrick Long (ORCID: https://orcid.org/0000-0002-7206-4607)
- Nicola Boulter
- Merina Su (ORCID: https://orcid.org/0000-0002-6486-898X)
- Ralica Dimitrova (ORCID: https://orcid.org/0000-0002-6903-7990)
- C. Weber (ORCID: https://orcid.org/0000-0002-8659-5767)
- Andres Quintero (ORCID: https://orcid.org/0000-0002-0405-6445)
- Javier Lopez-Molina
Institutions
- Pfizer (United States) (US)
- IQVIA (United Kingdom) (GB)
Publication Details
- Journal
- BMJ Health & Care Informatics
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1136/bmjhci-2024-101282
- Citations
- 1
- Primary Topic
- Asthma and respiratory diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.16