Unsupervised Machine Learning Identifies Asthma Phenotypes in the Population-Based West Sweden Asthma Study

Clin Exp Allergy. 2026 Feb;56(2):170-172. doi: 10.1111/cea.70161. Epub 2025 Oct 15.

Muwada Bashir Awad Bashir¹, Daniil Lisik^{1

2}, Saliha Selin Ozuygur Ermis¹, Rani Basna³, Reshed Abohalaka¹, Selin Ercan¹, Helena Backman², Teet Pullerits^{1

4}, Roxana Mincheva¹, Göran Wennergren⁵, Madeleine Rådinger¹, Jan Lötvall¹, Linda Ekerljung¹, Hannu Kankaanranta^{1

6

7}, Bright I Nwaru^{1

8}

¹ Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
² Department of Public Health and Clinical Medicine/the OLIN Unit, Umeå University, Umeå, Sweden.
³ Division of Geriatric Medicine, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden.
⁴ Section of Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden.
⁵ Department of Pediatrics, University of Gothenburg, Gothenburg, Sweden.
⁶ Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland.
⁷ Faculty of Medicine and Health Technology, Tampere University Respiratory Research Group, Tampere University, Tampere, Finland.
⁸ Wallenberg Centre for Molecular and Translational Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.

No abstract available

Keywords: Sweden; adults; asthma; cluster analysis; deep learning; phenotypes; population‐based study; unsupervised clustering.

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