Ellen Tolestam Heyman
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- 2025
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Mark
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
- Contribution to journal › Article
- 2024
-
Mark
A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system
- Contribution to journal › Article
-
Mark
Exploring Artificial Intelligence in Emergency Medicine for Predicting Disposition, Death, and Diagnosis
(2024) In Lund University, Faculty of Medicine Doctoral Dissertation Series
- Thesis › Doctoral thesis (compilation)
-
Mark
How does an AI diagnose dyspnoea in ED triage without human guidance?
(2024) Swedish Emergency Medicine Talks - SWEETS24
- Contribution to conference › Poster
- 2023
-
Mark
Interpretable AI diagnostics for dyspnea in the emergency department by deep learning and a massive regional health care dataset
(2023) Swedish Emergency Medicine Talks - SWEETS23
- Contribution to conference › Poster
-
Mark
Design of an AI Support for Diagnosis of Dyspneic Adults at Time of Triage in the Emergency Department
(2023) European Emergency Medicine Congress 2023
- Contribution to conference › Poster
- 2021
-
Mark
Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths
- Contribution to journal › Article
-
Mark
Likelihood of admission to hospital from the emergency department is not universally associated with hospital bed occupancy at the time of admission
- Contribution to journal › Article
