Yilin Ning

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 35

Top Topics

Machine Learning

Artificial Intelligence In Medicine

Top Venues

J. Biomed. Informatics

Artif. Intell. Medicine

Publications

Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning, Mingxuan Liu, Feng Xie, Marcus Eng Hock Ong, Nan Liu
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission. CoRR (2024)
Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare. CoRR (2024)
Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong, Danielle S. Bitterman, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting, Nan Liu
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine. CoRR (2024)
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
Towards Clinical AI Fairness: Filling Gaps in the Puzzle. CoRR (2024)
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 (2024)
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
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis. CoRR (2024)
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
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist. CoRR (2023)
Yilin Ning, Victor Volovici, Marcus Eng Hock Ong, Benjamin Alan Goldstein, Nan Liu
A roadmap to fair and trustworthy prediction model validation in healthcare. CoRR (2023)
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
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches. CoRR (2023)
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
A translational perspective towards clinical AI fairness. npj Digit. Medicine 6 (2023)
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. CoRR (2023)
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
FedScore: A privacy-preserving framework for federated scoring system development. J. Biomed. Informatics 146 (2023)
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
Towards clinical AI fairness: A translational perspective. CoRR (2023)
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
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artif. Intell. Medicine 142 (2023)
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) (2023)
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
FedScore: A privacy-preserving framework for federated scoring system development. CoRR (2023)
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 (2022)
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)
Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu
Shapley variable importance cloud for interpretable machine learning. Patterns 3 (4) (2022)
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)
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. J. Biomed. Informatics 126 (2022)
Seyed Ehsan Saffari, Yilin Ning, Xie Feng, 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 (2022)
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. J. Biomed. Informatics 129 (2022)
Yun Ke, Enzhe Song, Yanzhen Chen, Chong Yao, Yilin Ning
Multiscale Bidirectional Diversity Entropy for Diesel Injector Fault-Type Diagnosis and Fault Degree Diagnosis. IEEE Trans. Instrum. Meas. 71 (2022)
Mingxuan Liu, Yilin Ning, Han Yuan, Marcus Eng Hock Ong, Nan Liu
Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making. CoRR (2022)
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 (2022)
Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J. Biomed. Informatics 125 (2022)
Yilin Ning, Mingxuan Liu, Nan Liu
Shapley variable importance cloud for machine learning models. CoRR (2022)
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 (2021)
Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. CoRR (2021)
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 (2021)
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 (2021)
Jinwei Cai, Yilin Ning, Wenshi Li, Linhong Yao
Research on Anti-Noise Performance of New Chaos Criterion. DSA (2019)
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
Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workers. Int. J. Medical Informatics 120 (2018)
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
A de-identification tool for users in medical operations and public health. BHI (2016)