Bibhas Chakraborty

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 33

Top Topics

Machine Learning

Probabilistic Neural Networks

Top Venues

J. Biomed. Informatics

J. Am. Medical Informatics Assoc.

Publications

Nele Albers, Amal Abdulrahman, Deborah Richards, Caroline A. Figueroa, Bibhas Chakraborty, Ananya Bhattacharjee, Linwei He, Mark A. Neerincx, Joseph Jay Williams, Nezih Younsi, Tibor Bosse, Annemiek Linn, Crystal Smit, Willem-Paul Brinkman
Preface to the 1st International Workshop on Algorithmic Behavior Change Support. PERSUASIVE (Adjunct) (2024)
Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare. CoRR (2024)
Siqi Li, Yuqing Shang, Ziwen Wang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data. CoRR (2024)
Nina Deliu, Bibhas Chakraborty
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health. CoRR (2024)
Shraddha M. Naik, Tanujit Chakraborty, Abdenour Hadid, Bibhas Chakraborty
Skew Probabilistic Neural Networks for Learning from Imbalanced Data. CoRR (2023)
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
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. CoRR (2023)
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
FedScore: A privacy-preserving framework for federated scoring system development. J. Biomed. Informatics 146 (2023)
Xueqing Liu, Nina Deliu, Tanujit Chakraborty, Lauren Bell, Bibhas Chakraborty
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. CoRR (2023)
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
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artif. Intell. Medicine 142 (2023)
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
Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. J. Am. Medical Informatics Assoc. 30 (12) (2023)
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
FedScore: A privacy-preserving framework for federated scoring system development. CoRR (2023)
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 Ong, Fei Gao, Nan Liu
Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records. AMIA (2022)
Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu
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. CoRR (2022)
Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions. CoRR (2022)
Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu
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. AMIA (2022)
Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu
Shapley variable importance cloud for interpretable machine learning. Patterns 3 (4) (2022)
Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu
AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes. AMIA (2022)
Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. J. Biomed. Informatics 126 (2022)
Seyed Ehsan Saffari, Yilin Ning, Xie Feng, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes. CoRR (2022)
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
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. J. Biomed. Informatics 129 (2022)
Priyam Das, Debsurya De, Raju Maiti, Mona Kamal, Katherine A. Hutcheson, Clifton D. Fuller, Bibhas Chakraborty, Christine B. Peterson
Estimating the optimal linear combination of predictors using spherically constrained optimization. BMC Bioinform. (3) (2022)
Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. CoRR (2022)
Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J. Biomed. Informatics 125 (2022)
Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. CoRR (2021)
Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. CoRR (2021)
Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu
Shapley variable importance clouds for interpretable machine learning. CoRR (2021)
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
Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records. CoRR (2021)
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
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. CoRR (2021)
Caroline A. Figueroa, Adrián Aguilera, Bibhas Chakraborty, Arghavan Modiri, Jai Aggarwal, Nina Deliu, Urmimala Sarkar, Joseph Jay Williams, Courtney R. Lyles
Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. J. Am. Medical Informatics Assoc. 28 (6) (2021)
Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty, Palash Ghosh
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens. CoRR (2021)
Feng Xie, Bibhas Chakraborty, Nan Liu, Marcus Eng Hock Ong
Development and Validation of a Survival Score for the Emergency Department in Singapore. AMIA (2021)
Caroline A. Figueroa, Adrián Aguilera, Bibhas Chakraborty, Arghavan Modiri, Jai Aggarwal, Nina Deliu, Urmimala Sarkar, Joseph Jay Williams, Courtney R. Lyles
Challenges and opportunities of using reinforcement learning to optimize behavioral health interventions delivered via smartphones. AMIA (2020)
Raju Maiti, Atanu Biswas, Bibhas Chakraborty
Modelling of low count heavy tailed time series data consisting large number of zeros and ones. Stat. Methods Appl. 27 (3) (2018)