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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

Tolestam Heyman, Ellen LU orcid ; Ashfaq, Awais ; Ekelund, Ulf LU orcid ; Ohlsson, Mattias LU orcid ; Björk, Jonas LU orcid ; Schubert, Alexander Marcel ; Lingman, Markus and Khoshnood, Ardavan M. LU orcid (2025) In International Journal of Medical Informatics 202(October 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. Methods In this cross-sectional study, we included all ED visits of patients 18 years or older with dyspnea at two regional Swedish EDs 07/01/2017–12/31/2019. In-hospital or ED discharge notes were used as outcome labels, with a subset manually reviewed by experts. We analyzed data from a complete regional healthcare system,... (More)
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. Methods In this cross-sectional study, we included all ED visits of patients 18 years or older with dyspnea at two regional Swedish EDs 07/01/2017–12/31/2019. In-hospital or ED discharge notes were used as outcome labels, with a subset manually reviewed by experts. We analyzed data from a complete regional healthcare system, along with socioeconomic factors, using Hierarchical Attention Networks. Each patient displayed a unique set of variables important for diagnosing dyspnea. All patients’ unique variable sets were aggregated into a variable list. The top 100, 50, and 20 variables were tested in a simpler CatBoost model. Finally, performance was compared after adding medical expertise to the AI model. Results We included 10,869 visits, with 15.1% having AHF, 13.6% eCOPD, and 13.1% pneumonia. The median number of variables per unique ED visit was 187 (IQR 111–307). Aggregating the unique sets of variables resulted in a cohort list of 2,064 variables. The median micro AUROC was 87.8% (2.5–97.5 percentile; 86.4–89.3%). Age, ECGs, previous diagnoses, and medication were considered important by the AI model, while sex, vital signs, and socioeconomic factors were deemed almost non-predictive. Using the top 20 AI-selected variables, the AUROC was 86.6% (85.1–88.1%). Adding human medical expertise did not significantly change the AUROC. Conclusion 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. (Less)
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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Emergency medicine, Emergency department, Diagnostics, Artificial intelligence, Machine learning
in
International Journal of Medical Informatics
volume
202
issue
October 2025
article number
105969
publisher
Elsevier
external identifiers
  • pmid:40440912
ISSN
1386-5056
DOI
10.1016/j.ijmedinf.2025.105969
project
Resource Management in the Emergency Department by using Machine Learning
language
English
LU publication?
yes
id
9e18a706-d1ce-4c62-94a8-777d72b48254
date added to LUP
2025-05-29 20:16:55
date last changed
2025-06-03 03:31:06
@article{9e18a706-d1ce-4c62-94a8-777d72b48254,
  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. Methods In this cross-sectional study, we included all ED visits of patients 18 years or older with dyspnea at two regional Swedish EDs 07/01/2017–12/31/2019. In-hospital or ED discharge notes were used as outcome labels, with a subset manually reviewed by experts. We analyzed data from a complete regional healthcare system, along with socioeconomic factors, using Hierarchical Attention Networks. Each patient displayed a unique set of variables important for diagnosing dyspnea. All patients’ unique variable sets were aggregated into a variable list. The top 100, 50, and 20 variables were tested in a simpler CatBoost model. Finally, performance was compared after adding medical expertise to the AI model. Results We included 10,869 visits, with 15.1% having AHF, 13.6% eCOPD, and 13.1% pneumonia. The median number of variables per unique ED visit was 187 (IQR 111–307). Aggregating the unique sets of variables resulted in a cohort list of 2,064 variables. The median micro AUROC was 87.8% (2.5–97.5 percentile; 86.4–89.3%). Age, ECGs, previous diagnoses, and medication were considered important by the AI model, while sex, vital signs, and socioeconomic factors were deemed almost non-predictive. Using the top 20 AI-selected variables, the AUROC was 86.6% (85.1–88.1%). Adding human medical expertise did not significantly change the AUROC. Conclusion 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.}},
  author       = {{Tolestam Heyman, Ellen and Ashfaq, Awais and Ekelund, Ulf and Ohlsson, Mattias and Björk, Jonas and Schubert, Alexander Marcel and Lingman, Markus and Khoshnood, Ardavan M.}},
  issn         = {{1386-5056}},
  keywords     = {{Emergency medicine; Emergency department; Diagnostics; Artificial intelligence; Machine learning}},
  language     = {{eng}},
  month        = {{05}},
  number       = {{October 2025}},
  publisher    = {{Elsevier}},
  series       = {{International Journal of Medical Informatics}},
  title        = {{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}},
  url          = {{https://lup.lub.lu.se/search/files/220194267/2025_Utilizing_artificial_intelligence_and_medical_experts_to_identify_predictors.pdf}},
  doi          = {{10.1016/j.ijmedinf.2025.105969}},
  volume       = {{202}},
  year         = {{2025}},
}