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.

Journal: International journal of medical informatics
Published Date: May 22, 2025

Abstract

OBJECTIVE: Half of all adult emergency department (ED) visits with a complaint of dyspnea involve acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), or pneumonia, which are often misdiagnosed. We aimed to create an artificial intelligence (AI) diagnostic decision support tool to detect patients with AHF, eCOPD, and pneumonia among dyspneic adults at the beginning of their ED visit.

Authors

  • Ellen T Heyman
    Department of Emergency Medicine, Halland Hospital, Region Halland, Sweden.
  • Awais Ashfaq
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden; Halland Hospital, Region Halland, Sweden. Electronic address: awais.ashfaq@hh.se.
  • Ulf Ekelund
    Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden; Skåne University Hospital Lund, Lund, Sweden.
  • Mattias Ohlsson
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
  • Jonas Björk
    Department of Clinical Sciences, Lund University, Sweden.
  • Alexander Marcel Schubert
    Department of Computational Precision Health, University of California, Berkeley, USA; Department of Computational Precision Health, University of California, San Francisco, USA. Electronic address: alexander_schubert@berkeley.edu.
  • Markus Lingman
    Halland Hospital, Region Halland, Sweden; Institute of Medicine, Dept. of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Sweden.
  • Ardavan M Khoshnood
    Emergency Medicine, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.

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