Bibhas Chakraborty

39 papers Misc 5Journal 33Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	BFTS: Thompson Sampling with Bayesian Additive Regression Trees. CoRR Ruizhe Deng, Bibhas Chakraborty, Ran Chen, Yan Shuo Tan
2026	J	`jnl`	Toward Global Large Language Models in Medicine. CoRR Rui Yang, Huitao Li, Weihao Xuan, Heli Qi, Xin Li, Kunyu Yu, Yingjian Chen, Rongrong Wang, Jacques Behmoaras, Tianxi Cai, Bibhas Chakraborty, Qingyu Chen, Lionel Tim-Ee Cheng, Marie-Louise Damwanza, Chido Dzinotyiwei, Aosong Feng, Chuan Hong, Yusuke Iwasawa, Yuhe Ke, Linah Kitala, Taehoon Ko, Jisan Lee, Irene Li, Jonathan Chong Kai Liew, Hongfang Liu, Lian Leng Low, Edison Marrese-Taylor, Yutaka Matsuo, Isheanesu Misi, Yilin Ning, Jasmine Chiat Ling Ong, Marcus Eng Hock Ong, Enrico Petretto, Hossein Rouhizadeh, Abiram Sandralegar, Oren Schreier, Iain Bee Huat Tan, Patrick Tan, Daniel Shu Wei Ting, Junjue Wang, Chunhua Weng, Matthew Yu Heng Wong, Fang Wu, Yunze Xiao, Xuhai Xu, Qingcheng Zeng, Zhuo Zheng, Yifan Peng, Douglas Teodoro, Nan Liu
2025	J	`jnl`	Developing federated time-to-event scores using heterogeneous real-world survival data. Comput. Biol. Medicine Siqi Li, Ziwen Wang, Yuqing Shang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu
2025	J	`jnl`	RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records. CoRR Yang Yang, Kathryn I. Pollak, Bibhas Chakraborty, Molei Liu, Doudou Zhou, Chuan Hong
2025	J	`jnl`	Skew-probabilistic neural networks for learning from imbalanced data. Pattern Recognit. Shraddha M. Naik, Tanujit Chakraborty, Madhurima Panja, Abdenour Hadid, Bibhas Chakraborty
2024	J	`jnl`	Artificial Intelligence-based Decision Support Systems for Precision and Digital Health. CoRR Nina Deliu, Bibhas Chakraborty
2024	J	`jnl`	Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data. CoRR Siqi Li, Yuqing Shang, Ziwen Wang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu
2024	J	`jnl`	FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare. Patterns Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
2024	J	`jnl`	Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare. CoRR Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
2024	—	`conf`	Preface to the 1st International Workshop on Algorithmic Behavior Change Support. PERSUASIVE (Adjunct) 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
2023	J	`jnl`	FedScore: A privacy-preserving framework for federated scoring system development. CoRR 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
2023	J	`jnl`	FedScore: A privacy-preserving framework for federated scoring system development. J. Biomed. Informatics 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
2023	J	`jnl`	Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. CoRR 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
2023	J	`jnl`	Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. J. Am. Medical Informatics Assoc. 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
2023	J	`jnl`	Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artif. Intell. Medicine 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
2023	J	`jnl`	Skew Probabilistic Neural Networks for Learning from Imbalanced Data. CoRR Shraddha M. Naik, Tanujit Chakraborty, Abdenour Hadid, Bibhas Chakraborty
2023	J	`jnl`	Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. CoRR Xueqing Liu, Nina Deliu, Tanujit Chakraborty, Lauren Bell, Bibhas Chakraborty
2022	Misc	`conf`	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 Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu
2022	J	`jnl`	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 Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu
2022	J	`jnl`	AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. J. Biomed. Informatics 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
2022	Misc	`conf`	AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes. AMIA Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu
2022	J	`jnl`	AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes. CoRR Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu
2022	J	`jnl`	AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J. Biomed. Informatics Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty
2022	Misc	`conf`	Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records. AMIA 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
2022	J	`jnl`	Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. J. Biomed. Informatics Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
2022	J	`jnl`	Estimating the optimal linear combination of predictors using spherically constrained optimization. BMC Bioinform. Priyam Das, Debsurya De, Raju Maiti, Mona Kamal, Katherine A. Hutcheson, Clifton D. Fuller, Bibhas Chakraborty, Christine B. Peterson
2022	J	`jnl`	Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. CoRR Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu
2022	J	`jnl`	Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions. CoRR Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
2022	J	`jnl`	Shapley variable importance cloud for interpretable machine learning. Patterns Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu
2021	J	`jnl`	A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens. CoRR Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty, Palash Ghosh
2021	J	`jnl`	Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. J. Am. Medical Informatics Assoc. Caroline A. Figueroa, Adrián Aguilera, Bibhas Chakraborty, Arghavan Modiri, Jai Aggarwal, Nina Deliu, Urmimala Sarkar, Joseph Jay Williams, Courtney R. Lyles
2021	J	`jnl`	AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. CoRR 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
2021	J	`jnl`	AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. CoRR Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty
2021	J	`jnl`	Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records. CoRR 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
2021	J	`jnl`	Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. CoRR Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
2021	Misc	`conf`	Development and Validation of a Survival Score for the Emergency Department in Singapore. AMIA Feng Xie, Bibhas Chakraborty, Nan Liu, Marcus Eng Hock Ong
2021	J	`jnl`	Shapley variable importance clouds for interpretable machine learning. CoRR Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu
2020	Misc	`conf`	Challenges and opportunities of using reinforcement learning to optimize behavioral health interventions delivered via smartphones. AMIA Caroline A. Figueroa, Adrián Aguilera, Bibhas Chakraborty, Arghavan Modiri, Jai Aggarwal, Nina Deliu, Urmimala Sarkar, Joseph Jay Williams, Courtney R. Lyles
2018	J	`jnl`	Modelling of low count heavy tailed time series data consisting large number of zeros and ones. Stat. Methods Appl. Raju Maiti, Atanu Biswas, Bibhas Chakraborty