Khaled Rjoob
According to our database1,
Khaled Rjoob
authored at least 14 papers
between 2019 and 2022.
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Bibliography
2022
Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications.
Artif. Intell. Medicine, 2022
2021
The effect of interpolating low amplitude leads on the inverse reconstruction of cardiac electrical activity.
Comput. Biol. Medicine, 2021
Estimating the Minimal Size of Training Datasets Required for the Development of Linear ECG-Lead Transformations.
Proceedings of the Computing in Cardiology, CinC 2021, Brno, 2021
2020
Computational time series analysis of patient referrals to a primary percutaneous coronary intervention service.
Health Informatics J., 2020
Improving the Detection of Acute Coronary Syndrome Using Machine Learning of Blood Biomarkers.
Proceedings of the Computing in Cardiology, 2020
Machine Learning to Predict 30 Days and 1-Year Mortality in STEMI and Turndown Patients.
Proceedings of the Computing in Cardiology, 2020
Regression or Pseudo-Inverse - Which Method Should be Preferred When Developing Inverse Linear ECG-Lead Transformations?
Proceedings of the Computing in Cardiology, 2020
Towards Explainable Artificial Intelligence and Explanation User Interfaces to Open the 'Black Box' of Automated ECG Interpretation.
Proceedings of the Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications, 2020
2019
Interpolating Low Amplitude ECG Signals Combined with Filtering According to International Standards Improves Inverse Reconstruction of Cardiac Electrical Activity.
Proceedings of the Functional Imaging and Modeling of the Heart, 2019
Role of dashboards in improving decision making in healthcare: Review of the literature.
Proceedings of the 31st European Conference on Cognitive Ergonomics, 2019
Machine Learning Improves the Detection of Misplaced V1 and V2 Electrodes During 12-Lead Electrocardiogram Acquisition.
Proceedings of the 46th Computing in Cardiology, 2019
Proceedings of the 46th Computing in Cardiology, 2019
Unsupervised Machine Learning Elicits Patient Archetypes in a Primary Percutaneous Coronary Intervention Service.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019
Predicting 30 days Mortality in STEMI Patients using Patient Referral Data to a Primary Percutaneous Coronary Intervention Service.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019