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. 2025 Oct:202:105969.
doi: 10.1016/j.ijmedinf.2025.105969. Epub 2025 May 22.

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

Ellen T Heyman  1 , Awais Ashfaq  2 , Ulf Ekelund  3 , Mattias Ohlsson  4 , Jonas Björk  5 , Alexander Marcel Schubert  6 , Markus Lingman  7 , Ardavan M Khoshnood  8
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Free article

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

Ellen T Heyman et al. Int J Med Inform. 2025 Oct.
Free article

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.

Keywords: Artificial intelligence; Diagnostics; Emergency department; Emergency medicine; Machine learning.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ellen Tolestam Heyman reports financial support was provided by Swedish Research Council. Ellen Tolestam Heyman reports financial support was provided by Region Halland. Ellen Tolestam Heyman reports financial support was provided by Sparbanksstiftelsen Varberg. Ellen Tolestam Heyman reports financial support was provided by Stiftelsen Landshövding Per Westlings minnesfond. Markus Lingman reports a relationship with Tandem Health Inc. that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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