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Ulf Ekelund

Lund University - Skane University Hospital

SCHOLARLY PAPERS

3

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137

TOTAL CITATIONS

0

Scholarly Papers (3)

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1.

A Novel Interpretable Deep Learning Model for Diagnosis in Emergency Department Dyspnoea Patients Based on Complete Data from an Entire Health Care System

Number of pages: 26 Posted: 03 Oct 2023
Ellen Tolestam Heyman, Awais Ashfaq, Ulf Ekelund, Mattias Ohlsson, Jonas Björk, Ardavan Khoshnood and Markus Lingman
Lund University, Halmstad University, Lund University - Skane University Hospital, Halmstad University - Center for Applied Intelligent Systems Research, Lund University - Department of Laboratory Medicine, Lund University and University of Gothenburg
Downloads 83 (796,622)

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Emergency Medicine, Emergency Department, Diagnostics, Artificial Intelligence, MACHINE LEARNING

2.

Prior Electrocardiograms not Useful for Predicting Major Adverse Cardiac Events with Machine Learning

Number of pages: 19 Posted: 09 Jul 2022
Axel Nyström, Pontus Olsson de Capretz, Anders Björkelund, Jakob Lundager Forberg, Mattias Ohlsson, Jonas Björk and Ulf Ekelund
Lund University - Department of Laboratory Medicine, Lund University - Skane University Hospital, Lund University, Lund University - Department of Clinical Sciences, Lund University, Lund University - Department of Laboratory Medicine and Lund University - Skane University Hospital
Downloads 31 (1,318,317)

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Machine Learning, neural networks, Emergency department, Chest pain, Major adverse cardiac event, Electrocardiograms

3.

Predicting Complications in Emergency Department Patients with Acute Coronary Syndrome – Existing Risk Scores versus a New Logistic Regression Model

Number of pages: 22 Posted: 20 Oct 2025
Tsvetelina Nilsson, Malin Strömfors, Alicia Trägårdh, Arash Mokhtari, Ardavan Khoshnood and Ulf Ekelund
affiliation not provided to SSRN, affiliation not provided to SSRN, Lund University, Lund University, Lund University and Lund University - Skane University Hospital
Downloads 23 (1,411,822)

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Acute coronary syndrome, emergency department, complications, predictors, risk stratification