| A machine learning based exploration of COVID-19 mortality risk M Mahdavi, H Choubdar, E Zabeh, M Rieder, S Safavi-Naeini, Z Jobbagy, ... Plos one 16 (7), e0252384, 2021 | 83 | 2021 |
| Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier N Amini, M Mahdavi, H Choubdar, A Abedini, A Shalbaf, R Lashgari Computer methods in biomechanics and biomedical engineering 26 (2), 160-173, 2023 | 8 | 2023 |
| Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study M Mahdavi, H Choubdar, Z Rostami, B Niroomand, AT Levine, A Fatemi, ... Scientific Reports 13 (1), 2827, 2023 | 6 | 2023 |
| Early prediction of COVID-19 mortality risk based on demographic, vital sign and blood test. A Shalbaf, N Amini, H Choubdar, M Mahdavi, A Abedini, R Lashgari | 2 | 2022 |
| Early detection of COVID-19 mortality risk using non-invasive clinical characteristics M Mahdavi, H Choubdar, E Zabeh, M Rieder, S Safavi-Naeini, ... | 2 | 2020 |
| Neural oscillatory characteristics of feedback-associated activity in globus pallidus interna H Choubdar, M Mahdavi, Z Rostami, E Zabeh, MJ Gillies, AL Green, ... Scientific Reports 13 (1), 4141, 2023 | | 2023 |
Mahdi MahdaviMcGill University在 mail.mcgill.ca 的电子邮件经过验证
