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AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.

H. Yuan, F. Xie, M. Ong, Y. Ning, M. Chee, S. Saffari, H. Abdullah, B. Goldstein, B. Chakraborty, and N. Liu. CoRR, (2021)

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Sourav Chakraborty

Anirban Chakraborty

Dipanjan Chakraborty

Diptangshu Chakraborty

Asok Chakraborty

Other publications of authors with the same name

Shapley variable importance clouds for interpretable machine learning.Y. Ning, M. Ong, B. Chakraborty, B. Goldstein, D. Ting, R. Vaughan, and N. Liu. CoRR, (2021)Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies.F. Xie, H. Yuan, Y. Ning, M. Ong, M. Feng, W. Hsu, B. Chakraborty, and N. Liu. CoRR, (2021)FedScore: A privacy-preserving framework for federated scoring system development.S. Li, Y. Ning, M. Ong, B. Chakraborty, C. Hong, F. Xie, H. Yuan, M. Liu, D. Buckland, Y. Chen and 1 other author(s). J. Biomed. Informatics, (October 2023)Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study.X. Liu, N. Deliu, T. Chakraborty, L. Bell, and B. Chakraborty. CoRR, (2023)Estimating the optimal linear combination of predictors using spherically constrained optimization.P. Das, D. De, R. Maiti, M. Kamal, K. Hutcheson, C. Fuller, B. Chakraborty, and C. Peterson. BMC Bioinform., 23-S (3): 436 (2022)Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records.F. Xie, J. Zhou, J. Lee, M. Tan, S. Li, L. Rajnthern, M. Chee, B. Chakraborty, A. Wong, A. Dagan and 3 other author(s). CoRR, (2021)Federated and distributed learning applications for electronic health records and structured medical data: A scoping review.S. Li, P. Liu, G. Nascimento, X. Wang, F. Leite, B. Chakraborty, C. Hong, Y. Ning, F. Xie, Z. Teo and 5 other author(s). CoRR, (2023)Shapley variable importance cloud for interpretable machine learning.Y. Ning, M. Ong, B. Chakraborty, B. Goldstein, D. Ting, R. Vaughan, and N. Liu. Patterns, 3 (4): 100452 (2022)Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare.M. Liu, Y. Ning, Y. Ke, Y. Shang, B. Chakraborty, M. Ong, R. Vaughan, and N. Liu. CoRR, (2024)Challenges and opportunities of using reinforcement learning to optimize behavioral health interventions delivered via smartphones.C. Figueroa, A. Aguilera, B. Chakraborty, A. Modiri, J. Aggarwal, N. Deliu, U. Sarkar, J. Williams, and C. Lyles. AMIA, AMIA, (2020)

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Disambiguation of "Chakraborty, Bibhas"

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AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.

Please choose a person to relate this publication to

Sourav Chakraborty

Anirban Chakraborty

Dipanjan Chakraborty

Diptangshu Chakraborty

Asok Chakraborty

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Chakraborty, Bibhas"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.

Please choose a person to relate this publication to

Sourav Chakraborty

Anirban Chakraborty

Dipanjan Chakraborty

Diptangshu Chakraborty

Asok Chakraborty

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.