A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system
We designed a novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients by analysing unselected data from a complete regional health care system.
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)
- 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)
- University of Gothenburg (SE)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1371/journal.pone.0311081
- Citations
- 4
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.76
Funders
- Vetenskapsrådet
- Forskning och Utveckling, Halland