| Machine learning‐based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics SE Awan, M Bennamoun, F Sohel, FM Sanfilippo, G Dwivedi ESC heart failure 6 (2), 428-435, 2019 | 149 | 2019 |
| Machine learning in heart failure: ready for prime time SE Awan, F Sohel, FM Sanfilippo, M Bennamoun, G Dwivedi Current opinion in cardiology 33 (2), 190-195, 2018 | 116 | 2018 |
| Imputation of missing data with class imbalance using conditional generative adversarial networks SE Awan, M Bennamoun, F Sohel, F Sanfilippo, G Dwivedi Neurocomputing 453, 164-171, 2021 | 83 | 2021 |
| Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death SE Awan, M Bennamoun, F Sohel, FM Sanfilippo, BJ Chow, G Dwivedi PloS one 14 (6), e0218760, 2019 | 78 | 2019 |
| A reinforcement learning-based approach for imputing missing data SE Awan, M Bennamoun, F Sohel, F Sanfilippo, G Dwivedi Neural Computing and Applications 34 (12), 9701-9716, 2022 | 22 | 2022 |
| Machine learningbased prediction of heart failure readmission or death: implications of choosing the right model and the right metrics. ESC Heart Fail 6 (2): 428–435 SE Awan, M Bennamoun, F Sohel, FM Sanfilippo, G Dwivedi | 5 | 2019 |
| Machine Learning with Applications to Heart Failure Data Analysis and Processing S Awan | | 2021 |
| Developing and testing a New Machine-Learning Method to identify patients with heart failure who are at risk of 30-Day readmission or mortality S Awan, M Bennamoun, F Sohel, SA Shah, J Rankin, F Sanfilippo, ... Heart, Lung and Circulation 27, S91, 2018 | | 2018 |
