Machine Learning Models for Differentiating Causes of Dyspnea in Emergency Department Patients: A Retrospective Study
ML algorithms show promise in supporting triage by distinguishing broad etiological categories of dyspnea, such as respiratory versus cardiac origins. While the models demonstrate useful classification performance, their limited sensitivity for specific diagnoses underscores the need for larger, more diverse datasets and advanced modeling approaches.
Authors
- Giorgia Lüthi-Corridori (ORCID: https://orcid.org/0000-0002-9956-3895)
- Justus Baarts
- Jörg D. Leuppi (ORCID: https://orcid.org/0000-0002-5554-0675)
- Maria Boesing (ORCID: https://orcid.org/0000-0001-8605-1893)
Institutions
- Harvard University (US)
- University of Basel (CH)
- University Hospital of Basel (CH)
- Kantonsspital Baselland (CH)
Publication Details
- Journal
- Journal of Multidisciplinary Healthcare
- Published
- 2025-10-01
- DOI
- https://doi.org/10.2147/jmdh.s550659
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00