Utilizing artificial intelligence and medical experts to identify predictors for common diagnoses in dyspneic adults: A cross-sectional study of consecutive emergency department patients from Southern Sweden
Based on the analysis of a high-dimensional dataset, we designed a lightweight 20-variable machine learning model that can early and effectively diagnose AHF, eCOPD, and pneumonia among ED patients with dyspnea.
Authors
- Awais Ashfaq (ORCID: https://orcid.org/0000-0001-5688-0156)
- Ellen Tolestam Heyman (ORCID: https://orcid.org/0000-0002-1106-1701)
- Jonas Björk (ORCID: https://orcid.org/0000-0003-1883-2000)
- Mattias Ohlsson (ORCID: https://orcid.org/0000-0003-1145-4297)
- Ulf Ekelund (ORCID: https://orcid.org/0000-0002-0951-1582)
- Ardavan Khoshnood (ORCID: https://orcid.org/0000-0002-3142-4119)
- Alexander Marcel Schubert
- Markus Lingman (ORCID: https://orcid.org/0000-0002-4068-0341)
Institutions
- Lund University (SE)
- Skåne University Hospital (SE)
- Hallands sjukhus Halmstad (SE)
- Halmstad University (SE)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-05-22
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.105969
- Citations
- 1
- Primary Topic
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Type
- article
- Field-Weighted Citation Impact
- 1.52