Bibhas Chakraborty

According to our database, Bibhas Chakraborty authored at least 24 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare.

Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu

CoRR, 2024

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data.

Siqi Li, Yuqing Shang, Ziwen Wang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu

CoRR, 2024

2023

Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.

Siqi Li, Pinyan Liu, Gustavo G. Nascimento, Xinru Wang, Fabio Renato Manzolli Leite, Bibhas Chakraborty, Chuan Hong, Yilin Ning, Feng Xie, Zhen Ling Teo, Daniel Shu Wei Ting, Hamed Haddadi, Marcus Eng Hock Ong, Marco Aurélio Peres, Nan Liu

J. Am. Medical Informatics Assoc., November, 2023

FedScore: A privacy-preserving framework for federated scoring system development.

Siqi Li, Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Chuan Hong, Feng Xie, Han Yuan, Mingxuan Liu, Daniel M. Buckland, Yong Chen, Nan Liu

J. Biomed. Informatics, October, 2023

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Yuqing Shang, Victor Volovici, Bibhas Chakraborty, Nan Liu

Artif. Intell. Medicine, August, 2023

Skew Probabilistic Neural Networks for Learning from Imbalanced Data.

Shraddha M. Naik, Tanujit Chakraborty, Abdenour Hadid, Bibhas Chakraborty

CoRR, 2023

Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study.

Xueqing Liu, Nina Deliu, Tanujit Chakraborty, Lauren Bell, Bibhas Chakraborty

CoRR, 2023

2022

Shapley variable importance cloud for interpretable machine learning.

Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu

Patterns, 2022

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

Han Yuan, Feng Xie, Marcus Eng Hock Ong, Yilin Ning, Marcel Lucas Chee, Seyed Ehsan Saffari, Hairil Rizal Abdullah, Benjamin Alan Goldstein, Bibhas Chakraborty, Nan Liu

J. Biomed. Informatics, 2022

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies.

Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu

J. Biomed. Informatics, 2022

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

Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty

J. Biomed. Informatics, 2022

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu

CoRR, 2022

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions.

Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty

CoRR, 2022

Estimating the optimal linear combination of predictors using spherically constrained optimization.

Priyam Das, Debsurya De, Raju Maiti, Mona Kamal, Katherine A. Hutcheson, Clifton D. Fuller, Bibhas Chakraborty, Christine B. Peterson

BMC Bioinform., 2022

Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records.

Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, Marcus Eng Hock Ong, Fei Gao, Nan Liu

Proceedings of the AMIA 2022, 2022

AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes.

Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu

Proceedings of the AMIA 2022, 2022

A Novel Interpretable Machine Learning System to Generate Clinical Risk Scores: An Application for Predicting Early Mortality or Unplanned Readmission in A Retrospective Cohort Study.

Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu

Proceedings of the AMIA 2022, 2022

2021

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

Caroline A. Figueroa, Adrián Aguilera, Bibhas Chakraborty, Arghavan Modiri, Jai Aggarwal, Nina Deliu, Urmimala Sarkar, Joseph Jay Williams, Courtney R. Lyles

J. Am. Medical Informatics Assoc., 2021

Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records.

Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, Marcus Eng Hock Ong, Fei Gao, Nan Liu

CoRR, 2021

Shapley variable importance clouds for interpretable machine learning.

Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu

CoRR, 2021

A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens.

Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty, Palash Ghosh

CoRR, 2021

Development and Validation of a Survival Score for the Emergency Department in Singapore.

Feng Xie, Bibhas Chakraborty, Nan Liu, Marcus Eng Hock Ong

Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020

Challenges and opportunities of using reinforcement learning to optimize behavioral health interventions delivered via smartphones.

Caroline A. Figueroa, Adrián Aguilera, Bibhas Chakraborty, Arghavan Modiri, Jai Aggarwal, Nina Deliu, Urmimala Sarkar, Joseph Jay Williams, Courtney R. Lyles

Proceedings of the AMIA 2020, 2020

2018

Modelling of low count heavy tailed time series data consisting large number of zeros and ones.

Raju Maiti, Atanu Biswas, Bibhas Chakraborty

Stat. Methods Appl., 2018