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Proceedings of the ACM Conference on Health, Inference, and Learning

Showing results (1-10 of 8) with videos related to

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Proceedings of the ACM Conference on Health, Inference, and Learning|November 16, 2020
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical ImagingLuke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|January 31, 2022
Variational Learning of Individual Survival DistributionsZidi Xiu, Chenyang Tao, Ricardo Henao
Proceedings of the ACM Conference on Health, Inference, and Learning|July 26, 2021
MMiDaS-AE: Multi-modal Missing Data aware Stacked Autoencoder for Biomedical Abstract ScreeningEric W Lee, Byron C Wallace, Karla I Galaviz, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|August 5, 2021
Deidentification of free-text medical records using pre-trained bidirectional transformersAlistair E W Johnson, Lucas Bulgarelli, Tom J Pollard
Proceedings of the ACM Conference on Health, Inference, and Learning|July 28, 2021
Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome DataThomas A Hooven, Adam Yun Chao Lin, Ansaf Salleb-Aouissi
Proceedings of the ACM Conference on Health, Inference, and Learning|December 7, 2020
Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural NetworksWei Zhang, Peggy Peissig, Zhaobin Kuang, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|March 4, 2021
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health RecordsArdavan Afshar, Ioakeim Perros, Haesun Park, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|July 26, 2021
CaliForest: Calibrated Random Forest for Health DataYubin Park, Joyce C Ho
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Proceedings of the ACM Conference on Health, Inference, and Learning|November 16, 2020
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical ImagingLuke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|January 31, 2022
Variational Learning of Individual Survival DistributionsZidi Xiu, Chenyang Tao, Ricardo Henao
Proceedings of the ACM Conference on Health, Inference, and Learning|July 26, 2021
MMiDaS-AE: Multi-modal Missing Data aware Stacked Autoencoder for Biomedical Abstract ScreeningEric W Lee, Byron C Wallace, Karla I Galaviz, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|August 5, 2021
Deidentification of free-text medical records using pre-trained bidirectional transformersAlistair E W Johnson, Lucas Bulgarelli, Tom J Pollard
Proceedings of the ACM Conference on Health, Inference, and Learning|July 28, 2021
Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome DataThomas A Hooven, Adam Yun Chao Lin, Ansaf Salleb-Aouissi
Proceedings of the ACM Conference on Health, Inference, and Learning|December 7, 2020
Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural NetworksWei Zhang, Peggy Peissig, Zhaobin Kuang, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|March 4, 2021
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health RecordsArdavan Afshar, Ioakeim Perros, Haesun Park, et al.
Proceedings of the ACM Conference on Health, Inference, and Learning|July 26, 2021
CaliForest: Calibrated Random Forest for Health DataYubin Park, Joyce C Ho
Pageof 1