Feasibility of machine learning analysis for the identification of patients with possible primary ciliary dyskinesia
This study demonstrates the feasibility of using ML to screen for PCD using claims data, even in the absence of a specific International Classification of Disease (ICD) code. While unvalidated, this work may serve as the basis for future ML efforts in rare disease detection. Such screening approaches may aid in the identification of individuals who may benefit from timely diagnostic testing and targeted interventions.
Authors
- Ruth-Anne Langan Pai (ORCID: https://orcid.org/0000-0001-9413-7611)
- Adam J. Shapiro (ORCID: https://orcid.org/0000-0001-6066-6750)
- Carey Kauffman
- Carlos Milla (ORCID: https://orcid.org/0000-0001-5515-3053)
- Michele Manion
- Michael G. O’Connor (ORCID: https://orcid.org/0000-0003-0571-8241)
- Gully Burns (ORCID: https://orcid.org/0000-0003-1493-865X)
- Heidi Bjornson-Pennell
Institutions
- Vanderbilt University (US)
- McGill University Health Centre (CA)
- The Dia Foundation for Research on Ageing Societies (JP)
- Chan Zuckerberg Initiative (United States) (US)
- Center for Excellence in Education (US)
- Stanford University (US)
Publication Details
- Journal
- Orphanet Journal of Rare Diseases
- Published
- 2025-10-14
- DOI
- https://doi.org/10.1186/s13023-025-03966-z
- Primary Topic
- Cystic Fibrosis Research Advances
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- National Center for Advancing Translational Sciences