Jakob Lundager Forberg

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OBJECTIVE Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. METHODS AND MATERIALS(More)
BACKGROUND Evaluation of emergency department (ED) performance remains a difficult task due to the lack of consensus on performance measures that reflects high quality, efficiency, and sustainability. AIM To describe, map, and critically evaluate which performance measures that the published literature regard as being most relevant in assessing overall ED(More)
Artificial neural network (ANN) ensembles have long suffered from a lack of interpretability. This has severely limited the practical usability of ANNs in settings where an erroneous decision can be disastrous. Several attempts have been made to alleviate this problem. Many of them are based on decomposing the decision boundary of the ANN into a set of(More)
BACKGROUND AND PURPOSE The purpose of this study was to determine which leads in the standard 12-lead electrocardiogram (ECG) are the best for detecting acute coronary syndrome (ACS) among chest pain patients in the emergency department. METHODS Neural network classifiers were used to determine the predictive capability of individual leads and(More)
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