A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system.

Journal: PloS one
PMID: 39729465

Abstract

BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), pneumonia and "other diagnoses" by using deep learning and complete, unselected data from an entire regional health care system.

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.
  • Ardavan M Khoshnood
    Emergency Medicine, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
  • Markus Lingman
    Halland Hospital, Region Halland, Sweden; Institute of Medicine, Dept. of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Sweden.

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