148 citations · 183 across the 11 of their papers we have counts for
7 papers · 1 filter
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
Mingxuan Liu, Yilin Ning, Yuhe Ke +5
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework - Fa…
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 +5
Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analys…
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
Yilin Ning, Salinelat Teixayavong, Yuqing Shang +14
The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially i…
FedScore: A privacy-preserving framework for federated scoring system development
Siqi Li, Yilin Ning, Marcus Eng Hock Ong +8
We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. The Fed…
Shapley variable importance cloud for machine learning models
Yilin Ning, Mingxuan Liu, Nan Liu
Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e.g., by using the Shapley additive explanations (SHAP) method. Th…
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Mingxuan Liu, Siqi Li, Han Yuan +7
Objective: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. The increas…
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 +2
Objective: Shapley additive explanations (SHAP) is a popular post-hoc technique for explaining black box models. While the impact of data imbalance on predictive models has been ex…