A deep active survival analysis approach for precision treatment recommendations: Application of prostate cancer MZ Nezhad, N Sadati, K Yang, D Zhu Expert Systems with Applications 115, 16-26, 2019 | 73 | 2019 |
Observational data-driven modeling and optimization of manufacturing processes N Sadati, RB Chinnam, MZ Nezhad Expert Systems with Applications 93, 456-464, 2018 | 72 | 2018 |
SUBIC: A supervised bi-clustering approach for precision medicine MZ Nezhad, D Zhu, N Sadati, K Yang, P Levi 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 29 | 2017 |
Representation learning with autoencoders for electronic health records: a comparative study N Sadati, MZ Nezhad, RB Chinnam, D Zhu arXiv preprint arXiv:1801.02961, 2018 | 28* | 2018 |
A Predictive Approach Using Deep Feature Learning for Electronic Medical Records: A Comparative Study M Zafar Nezhad, D Zhu, N Sadati, K Yang arXiv preprint arXiv:1801.02961, 2018 | 1 | 2018 |
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer M Zafar Nezhad, N Sadati, K Yang, D Zhu arXiv e-prints, arXiv: 1804.03280, 2018 | | 2018 |
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine M Zafar Nezhad, D Zhu, N Sadati, K Yang, P Levy arXiv e-prints, arXiv: 1709.09929, 2017 | | 2017 |