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)

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 (AMIA, 2020)

Development and Validation of a Survival Score for the Emergency Department in Singapore. - Feng Xie 004; Bibhas Chakraborty; Nan Liu 003; Marcus Eng Hock Ong (AMIA, 2021)

A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens. - Trikay Nalamada; Shruti Agarwal; Maria Jahja; Bibhas Chakraborty; Palash Ghosh (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 003 (CoRR, 2021)

Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records. - Feng Xie 004; Jun Zhou 014; Jin Wee Lee; Mingrui Tan; Siqi Li 004; Logasan S/O Rajnthern; Marcel Lucas Chee; Bibhas Chakraborty; An-Kwok Ian Wong; Alon Dagan; Marcus Eng Hock Ong; Fei Gao; et al. (CoRR, 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)

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 004; Marcus Eng Hock Ong; Feng Xie 004; Bibhas Chakraborty; Daniel Shu Wei Ting; Nan Liu 003 (AMIA, 2022)

AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes. - Seyed Ehsan Saffari; Yilin Ning; Feng Xie 004; Bibhas Chakraborty; Victor Volovici; Roger Vaughan; Marcus Eng Hock Ong; Nan Liu 003 (AMIA, 2022)

Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records. - Feng Xie 004; Jun Zhou 014; Jin Wee Lee; Mingrui Tan; Siqi Li 004; Logasan S/O Rajnthern; Marcel Lucas Chee; Bibhas Chakraborty; An-Kwok Ian Wong; Alon Dagan; Marcus Eng Hock Ong; Fei Gao; et al. (AMIA, 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)

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions. - Nina Deliu; Joseph Jay Williams; Bibhas Chakraborty (CoRR, 2022)

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. - Mingxuan Liu; Siqi Li 004; Han Yuan; Marcus Eng Hock Ong; Yilin Ning; Feng Xie 004; Seyed Ehsan Saffari; Victor Volovici; Bibhas Chakraborty; Nan Liu 003 (CoRR, 2022)

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. - Feng Xie 004; Yilin Ning; Han Yuan; Benjamin Alan Goldstein; Marcus Eng Hock Ong; Nan Liu 003; Bibhas Chakraborty (J. Biomed. Informatics, 2022)

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. - Feng Xie 004; Han Yuan; Yilin Ning; Marcus Eng Hock Ong; Mengling Feng; Wynne Hsu; Bibhas Chakraborty; Nan Liu 003 (J. Biomed. Informatics, 2022)

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. - Han Yuan; Feng Xie 004; Marcus Eng Hock Ong; Yilin Ning; Marcel Lucas Chee; Seyed Ehsan Saffari; Hairil Rizal Abdullah; Benjamin Alan Goldstein; Bibhas Chakraborty; Nan Liu 003 (J. Biomed. Informatics, 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 003 (Patterns, 2022)

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)

Skew Probabilistic Neural Networks for Learning from Imbalanced Data. - Shraddha M. Naik; Tanujit Chakraborty; Abdenour Hadid; Bibhas Chakraborty (CoRR, 2023)

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. - Mingxuan Liu; Siqi Li 004; Han Yuan; Marcus Eng Hock Ong; Yilin Ning; Feng Xie 004; Seyed Ehsan Saffari; Yuqing Shang; Victor Volovici; Bibhas Chakraborty; Nan Liu 003 (Artif. Intell. Medicine, 2023)

FedScore: A privacy-preserving framework for federated scoring system development. - Siqi Li 004; Yilin Ning; Marcus Eng Hock Ong; Bibhas Chakraborty; Chuan Hong; Feng Xie 004; Han Yuan; Mingxuan Liu; Daniel M. Buckland; Yong Chen 016; Nan Liu 003 (J. Biomed. Informatics, 2023)

Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. - Siqi Li 004; Pinyan Liu; Gustavo G. Nascimento; Xinru Wang; Fabio Renato Manzolli Leite; Bibhas Chakraborty; Chuan Hong; Yilin Ning; Feng Xie 004; Zhen Ling Teo; Daniel Shu Wei Ting; Hamed Haddadi; et al. (J. Am. Medical Informatics Assoc., 2023)

Preface to the 1st International Workshop on Algorithmic Behavior Change Support. - Nele Albers; Amal Abdulrahman; Deborah Richards 001; Caroline A. Figueroa; Bibhas Chakraborty; Ananya Bhattacharjee; Linwei He; Mark A. Neerincx; Joseph Jay Williams; Nezih Younsi; Tibor Bosse; Annemiek Linn; et al. (PERSUASIVE (Adjunct), 2024)

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data. - Siqi Li 004; Yuqing Shang; Ziwen Wang; Qiming Wu 001; Chuan Hong; Yilin Ning; Di Miao; Marcus Eng Hock Ong; Bibhas Chakraborty; Nan Liu 003 (CoRR, 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 003 (CoRR, 2024)

Artificial Intelligence-based Decision Support Systems for Precision and Digital Health. - Nina Deliu; Bibhas Chakraborty (CoRR, 2024)

FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare. - Mingxuan Liu; Yilin Ning; Yuhe Ke; Yuqing Shang; Bibhas Chakraborty; Marcus Eng Hock Ong; Roger Vaughan; Nan Liu 003 (Patterns, 2024)

Skew-probabilistic neural networks for learning from imbalanced data. - Shraddha M. Naik; Tanujit Chakraborty; Madhurima Panja; Abdenour Hadid; Bibhas Chakraborty (Pattern Recognit., 2025)