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AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

F. Xie, Y. Ning, H. Yuan, B. Goldstein, M. Ong, N. Liu, and B. Chakraborty. J. Biomed. Informatics, (2022)

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Sourav Chakraborty

Dipanjan Chakraborty

Anirban Chakraborty

Diptangshu Chakraborty

Asok Chakraborty

Other publications of authors with the same name

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)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)Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.M. Liu, S. Li, H. Yuan, M. Ong, Y. Ning, F. Xie, S. Saffari, Y. Shang, V. Volovici, B. Chakraborty and 1 other author(s). Artif. Intell. Medicine, (August 2023)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. J. Biomed. Informatics, (2022)Benchmarking Emergency Department Triage Prediction Models with Machine Learning and 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). AMIA, AMIA, (2022)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)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)

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Disambiguation of "Chakraborty, Bibhas"

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AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Please choose a person to relate this publication to

Sourav Chakraborty

Dipanjan Chakraborty

Anirban 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-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Please choose a person to relate this publication to

Sourav Chakraborty

Dipanjan Chakraborty

Anirban 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
Harvard
MSOffice XML

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.