Clinical Trial Active Learning

Author:

Fowler Zoe1ORCID,Kokilepersaud Kiran Premdat1ORCID,Prabhushankar Mohit1ORCID,Alregib Ghassan1ORCID

Affiliation:

1. Georgia Institute of Technology, Atlanta, Georgia, USA

Funder

National Science Foundation

Publisher

ACM

Reference31 articles.

1. Jordan T Ash Chicheng Zhang Akshay Krishnamurthy John Langford and Alekh Agarwal. 2019. Deep batch active learning by diverse uncertain gradient lower bounds. arXiv preprint arXiv:1906.03671. Jordan T Ash Chicheng Zhang Akshay Krishnamurthy John Langford and Alekh Agarwal. 2019. Deep batch active learning by diverse uncertain gradient lower bounds. arXiv preprint arXiv:1906.03671.

2. Patrick Bangert , Hankyu Moon , Jae Oh Woo , Sima Didari, and Heng Hao. 2021 . Active learning performance in labeling radiology images is 90% effective. Frontiers in radiology, 1, 748968. Patrick Bangert, Hankyu Moon, Jae Oh Woo, Sima Didari, and Heng Hao. 2021. Active learning performance in labeling radiology images is 90% effective. Frontiers in radiology, 1, 748968.

3. Gaussian switch sampling: a second order approach to active learning;Benkert Ryan;IEEE Transactions on Artificial Intelligence.,2023

4. Multi-disciplinary fairness considerations in machine learning for clinical trials

5. Amit Choudhary , Savita Ahlawat , Shabana Urooj , Nitish Pathak , Aimé Lay-Ekuakille , and Neelam Sharma . 2023. A deep learning-based framework for retinal disease classification . In Healthcare number 2. Vol. 11 . MDPI , 212. Amit Choudhary, Savita Ahlawat, Shabana Urooj, Nitish Pathak, Aimé Lay-Ekuakille, and Neelam Sharma. 2023. A deep learning-based framework for retinal disease classification. In Healthcare number 2. Vol. 11. MDPI, 212.

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1. Active learning for data streams: a survey;Machine Learning;2023-11-20

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