Yilin Ning

According to our database, Yilin Ning authored at least 40 papers between 2016 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Toward Global Large Language Models in Medicine.

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

CoRR, January, 2026

Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority.

Jasmine Chiat Ling Ong, Yilin Ning, Mingxuan Liu, Yian Ma, Liang Zhao, Kuldev Singh, Robert T. Chang, Silke Vogel, John CW Lim, Iris Siu Kwan Tan, Oscar Freyer, Stephen Gilbert, Danielle S. Bitterman, Xiaoxuan Liu, Alastair K. Denniston, Nan Liu

npj Digit. Medicine, 2026

2025

Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach.

Mingxuan Liu, Yilin Ning, Haoyuan Wang, Chuan Hong, Matthew Engelhard, Danielle S. Bitterman, William G. La Cava, Nan Liu

CoRR, October, 2025

Gender Bias in Large Language Models for Healthcare: Assignment Consistency and Clinical Implications.

Mingxuan Liu, Yuhe Ke, Wentao Zhu, Mayli Mertens, Yilin Ning, Jingchi Liao, Chuan Hong, Daniel Shu Wei Ting, Yifan Peng, Danielle S. Bitterman, Marcus Eng Hock Ong, Nan Liu

CoRR, October, 2025

seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness.

Yilin Ning, Yian Ma, Mingxuan Liu, Xin Li, Nan Liu

CoRR, April, 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, Danielle S. Bitterman, Xiaoxuan Liu, Alastair K. Denniston, Nan Liu

CoRR, February, 2025

⁠Advancing ethical AI in healthcare through interpretability.

Yilin Ning, Mingxuan Liu, Nan Liu

Patterns, 2025

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, Di Miao, Jingchi Liao, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Narrendar Ravichandran, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

npj Digit. Medicine, 2025

Developing federated time-to-event scores using heterogeneous real-world survival data.

Siqi Li, Ziwen Wang, Yuqing Shang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu

Comput. Biol. Medicine, 2025

FairFML: A Unified Approach to Algorithmic Fair Federated Learning with Applications to Reducing Gender Disparities in Cardiac Arrest Outcomes.

Siqi Li, Qiming Wu, Xin Li, Di Miao, Chuan Hong, Wenjun Gu, Yilin Ning, Yuqing Shang, Nan Liu

Proceedings of the MEDINFO 2025 - Healthcare Smart × Medicine Deep, 2025

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

Patterns, 2024

Disparities in clinical studies of AI enabled applications from a global perspective.

Rui Yang, Sabarinath Vinod Nair, Yuhe Ke, Danny D'Agostino, Mingxuan Liu, Yilin Ning, Nan Liu

npj Digit. Medicine, 2024

FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction.

Siqi Li, Qiming Wu, Xin Li, Di Miao, Chuan Hong, Wenjun Gu, Yuqing Shang, Yohei Okada, Michael Hao Chen, Mengying Yan, Yilin Ning, Marcus Eng Hock Ong, Nan Liu

CoRR, 2024

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis.

Siqi Li, Xin Li, Kunyu Yu, Di Miao, Mingcheng Zhu, Mengying Yan, Yuhe Ke, Danny D'Agostino, Yilin Ning, Qiming Wu, Ziwen Wang, Yuqing Shang, Molei Liu, Chuan Hong, Nan Liu

CoRR, 2024

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine.

Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong, Danielle S. Bitterman, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting, Nan Liu

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, Di Miao, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Narrendar RaviChandran, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

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, Marcus Eng Hock Ong, Nan Liu

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

CoRR, 2024

2023

Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.

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

J. Am. Medical Informatics Assoc., November, 2023

FedScore: A privacy-preserving framework for federated scoring system development.

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

J. Biomed. Informatics, October, 2023

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

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

Artif. Intell. Medicine, August, 2023

A translational perspective towards clinical AI fairness.

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Ravi Chandran Narrendar, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

npj Digit. Medicine, 2023

Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches.

Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D'Agostino, Xin Li, Yilin Ning, Yuqing Shang, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu

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, Roger Vaughan, Marcus Eng Hock Ong, Joseph Jao Yiu Sung, Eric J. Topol, Nan Liu

CoRR, 2023

Towards clinical AI fairness: A translational perspective.

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Ravi Chandran Narrendar, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu

CoRR, 2023

A roadmap to fair and trustworthy prediction model validation in healthcare.

Yilin Ning, Victor Volovici, Marcus Eng Hock Ong, Benjamin Alan Goldstein, Nan Liu

CoRR, 2023

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

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

Patterns, 2022

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.

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

J. Biomed. Informatics, 2022

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies.

Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu

J. Biomed. Informatics, 2022

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty

J. Biomed. Informatics, 2022

Shapley variable importance cloud for machine learning models.

Yilin Ning, Mingxuan Liu, Nan Liu

CoRR, 2022

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu

CoRR, 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

CoRR, 2022

AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes.

Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu

Proceedings of the AMIA 2022, 2022

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, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu

Proceedings of the AMIA 2022, 2022

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

CoRR, 2021

2019

Research on Anti-Noise Performance of New Chaos Criterion.

Jinwei Cai, Yilin Ning, Wenshi Li, Linhong Yao

Proceedings of the 6th International Conference on Dependable Systems and Their Applications, 2019

2018

Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workers.

Ying Chen, 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

2016

A de-identification tool for users in medical operations and public health.

Mark Kevin Salloway, Xiaodong Deng, Yilin Ning, Shih Ling Kao, Ying Chen, G. Owen Schaefer, Jacqueline Joon-Lin Chin, E. Shyong Tai, Chuen Seng Tan

Proceedings of the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics, 2016