A de-identification tool for users in medical operations and public health. - Mark Kevin Salloway; Xiaodong Deng; Yilin Ning; Shih Ling Kao; Ying Chen 009; G. Owen Schaefer; Jacqueline Joon-Lin Chin; E. Shyong Tai; Chuen Seng Tan (BHI, 2016)

Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workers. - Ying Chen 009; Shih Ling Kao; Maudrene Tan; Yilin Ning; Mark Kevin Salloway; Wee Hwee Lin; Kavita Venkataraman; Eric Khoo Yin Hao; Yeow Leng Chow; E. Shyong Tai; Chuen Seng Tan (Int. J. Medical Informatics, 2018)

Research on Anti-Noise Performance of New Chaos Criterion. - Jinwei Cai; Yilin Ning; Wenshi Li; Linhong Yao (DSA, 2019)

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)

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)

Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making. - Mingxuan Liu; Yilin Ning; Han Yuan; Marcus Eng Hock Ong; Nan Liu 003 (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)

Shapley variable importance cloud for machine learning models. - Yilin Ning; Mingxuan Liu; 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)

Multiscale Bidirectional Diversity Entropy for Diesel Injector Fault-Type Diagnosis and Fault Degree Diagnosis. - Yun Ke; Enzhe Song; Yanzhen Chen; Chong Yao; Yilin Ning (IEEE Trans. Instrum. Meas., 2022)

A roadmap to fair and trustworthy prediction model validation in healthcare. - Yilin Ning; Victor Volovici; Marcus Eng Hock Ong; Benjamin Alan Goldstein; Nan Liu 003 (CoRR, 2023)

Towards clinical AI fairness: A translational perspective. - Mingxuan Liu; Yilin Ning; Salinelat Teixayavong; Mayli Mertens; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; Jasmine Chiat Ling Ong; Zhen Ling Teo; Ting Fang Tan; Ravi Chandran Narrendar; Fei Wang 001; et al. (CoRR, 2023)

Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist. - Yilin Ning; Salinelat Teixayavong; Yuqing Shang; Julian Savulescu; Vaishaanth Nagaraj; Di Miao; Mayli Mertens; Daniel Shu Wei Ting; Jasmine Chiat Ling Ong; Mingxuan Liu; Jiuwen Cao; Michael Dunn; et al. (CoRR, 2023)

Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches. - Siqi Li 004; Di Miao; Qiming Wu 001; Chuan Hong; Danny D'Agostino; Xin Li 079; Yilin Ning; Yuqing Shang; Huazhu Fu; Marcus Eng Hock Ong; Hamed Haddadi; Nan Liu 003 (CoRR, 2023)

A translational perspective towards clinical AI fairness. - Mingxuan Liu; Yilin Ning; Salinelat Teixayavong; Mayli Mertens; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; Jasmine Chiat Ling Ong; Zhen Ling Teo; Ting Fang Tan; Ravi Chandran Narrendar; Fei Wang 001; et al. (npj Digit. Medicine, 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)

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)

Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission. - Ziwen Wang; Jin Wee Lee; Tanujit Chakraborty; Yilin Ning; Mingxuan Liu; Feng Xie 004; Marcus Eng Hock Ong; Nan Liu 003 (CoRR, 2024)

Towards Clinical AI Fairness: Filling Gaps in the Puzzle. - Mingxuan Liu; Yilin Ning; Salinelat Teixayavong; Xiaoxuan Liu; Mayli Mertens; Yuqing Shang; Xin Li 079; Di Miao; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; Jasmine Chiat Ling Ong; et al. (CoRR, 2024)

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine. - Rui Yang 016; Yilin Ning; Emilia Keppo; Mingxuan Liu; Chuan Hong; Danielle S. Bitterman; Jasmine Chiat Ling Ong; Daniel Shu Wei Ting; Nan Liu 003 (CoRR, 2024)

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis. - Siqi Li 004; Xin Li 079; Kunyu Yu; Di Miao; Mingcheng Zhu 003; Mengying Yan; Yuhe Ke; Danny D'Agostino; Yilin Ning; Qiming Wu 001; Ziwen Wang; Yuqing Shang; et al. (CoRR, 2024)

FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction. - Siqi Li 004; Qiming Wu 001; Xin Li 079; Di Miao; Chuan Hong; Wenjun Gu; Yuqing Shang; Yohei Okada; Michael Hao Chen; Mengying Yan; Yilin Ning; Marcus Eng Hock Ong; et al. (CoRR, 2024)

Disparities in clinical studies of AI enabled applications from a global perspective. - Rui Yang 016; Sabarinath Vinod Nair; Yuhe Ke; Danny D'Agostino; Mingxuan Liu; Yilin Ning; Nan Liu 003 (npj Digit. Medicine, 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)

A scoping review and evidence gap analysis of clinical AI fairness. - Mingxuan Liu; Yilin Ning; Salinelat Teixayavong; Xiaoxuan Liu; Mayli Mertens; Yuqing Shang; Xin Li 079; Di Miao; Jingchi Liao; Jie Xu 012; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; et al. (npj Digit. Medicine, 2025)

⁠Advancing ethical AI in healthcare through interpretability. - Yilin Ning; Mingxuan Liu; Nan Liu 003 (Patterns, 2025)

Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action. - Jasmine Chiat Ling Ong; Yilin Ning; Mingxuan Liu; Yian Ma; Zhao Liang; Kuldev Singh; Robert T. Chang; Silke Vogel; John CW Lim; Iris Siu Kwan Tan; Oscar Freyer; Stephen Gilbert; et al. (CoRR, 2025)

seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness. - Yilin Ning; Yian Ma; Mingxuan Liu; Xin Li 079; Nan Liu 003 (CoRR, 2025)