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Bibhas Chakraborty
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2020 – today
- 2024
- [j11]Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu:
FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare. Patterns 5(10): 101059 (2024) - [c6]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: 1-7 - [i17]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 abs/2403.05229 (2024) - [i16]Mingxuan Liu, Yilin Ning
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare. CoRR abs/2403.05235 (2024) - [i15]Nina Deliu, Bibhas Chakraborty:
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health. CoRR abs/2407.16062 (2024) - 2023
- [j10]Mingxuan Liu, Siqi Li, Han Yuan
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artif. Intell. Medicine 142: 102587 (2023) - [j9]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): 2041-2049 (2023) - [j8]Siqi Li
FedScore: A privacy-preserving framework for federated scoring system development. J. Biomed. Informatics 146: 104485 (2023) - [i14]Siqi Li, Yilin Ning
FedScore: A privacy-preserving framework for federated scoring system development. CoRR abs/2303.00282 (2023) - [i13]Siqi Li, Pinyan Liu, Gustavo G. Nascimento, Xinru Wang, Fabio Renato Manzolli Leite, Bibhas Chakraborty, Chuan Hong, Yilin Ning
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. CoRR abs/2304.07310 (2023) - [i12]Xueqing Liu, Nina Deliu, Tanujit Chakraborty
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. CoRR abs/2311.14359 (2023) - [i11]Shraddha M. Naik, Tanujit Chakraborty
Skew Probabilistic Neural Networks for Learning from Imbalanced Data. CoRR abs/2312.05878 (2023) - 2022
- [j7]Priyam Das
Estimating the optimal linear combination of predictors using spherically constrained optimization. BMC Bioinform. 23-S(3): 436 (2022) - [j6]Feng Xie
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J. Biomed. Informatics 125: 103959 (2022) - [j5]Feng Xie
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. J. Biomed. Informatics 126: 103980 (2022) - [j4]Han Yuan
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. J. Biomed. Informatics 129: 104072 (2022) - [j3]Yilin Ning
Shapley variable importance cloud for interpretable machine learning. Patterns 3(4): 100452 (2022) - [c5]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 - [c4]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 - [c3]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 Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records. AMIA 2022 - [i10]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 abs/2201.03291 (2022) - [i9]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. CoRR abs/2202.08407 (2022) - [i8]Nina Deliu
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions. CoRR abs/2203.02605 (2022) - [i7]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 abs/2210.08258 (2022) - 2021
- [j2]Caroline A. Figueroa
Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. J. Am. Medical Informatics Assoc. 28(6): 1225-1234 (2021) - [c2]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 - [i6]Feng Xie
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. CoRR abs/2106.06957 (2021) - [i5]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 abs/2107.06039 (2021) - [i4]Trikay Nalamada, Shruti Agarwal, Maria Jahja, Bibhas Chakraborty, Palash Ghosh:
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens. CoRR abs/2107.07875 (2021) - [i3]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 abs/2107.09951 (2021) - [i2]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 abs/2110.02484 (2021) - [i1]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 abs/2111.11017 (2021) - 2020
- [c1]Caroline A. Figueroa, Adrián Aguilera, Bibhas Chakraborty, Arghavan Modiri, Jai Aggarwal, Nina Deliu, Urmimala Sarkar, Joseph Jay Williams
Challenges and opportunities of using reinforcement learning to optimize behavioral health interventions delivered via smartphones. AMIA 2020
2010 – 2019
- 2018
- [j1]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): 407-435 (2018)
Coauthor Index
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