160 citations · 491 across the 22 of their papers we have counts for
22 papers
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
Siqi Li, Qiming Wu, Xin Li +10
Objective: Mitigating algorithmic disparities is a critical challenge in healthcare research, where ensuring equity and fairness is paramount. While large-scale healthcare data exi…
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
Siqi Li, Xin Li, Kunyu Yu +12
Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effecti…
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine
Rui Yang, Yilin Ning, Emilia Keppo +6
Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-a…
Towards Clinical AI Fairness: Filling Gaps in the Puzzle
Mingxuan Liu, Yilin Ning, Salinelat Teixayavong +16
The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive st…
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…
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
Siqi Li, Yuqing Shang, Ziwen Wang +7
Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain dise…
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…
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Siqi Li, Di Miao, Qiming Wu +9
Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering commun…
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…
Towards clinical AI fairness: A translational perspective
Mingxuan Liu, Yilin Ning, Salinelat Teixayavong +12
Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despi…
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Siqi Li, Pinyan Liu, Gustavo G. Nascimento +12
Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms…
A roadmap to fair and trustworthy prediction model validation in healthcare
Yilin Ning, Victor Volovici, Marcus Eng Hock Ong +2
A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear. In practice, pre…
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…
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes
Seyed Ehsan Saffari, Yilin Ning, Xie Feng +5
Background: Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care fo…
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 +4
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models. Machine-learning-based methods may work well for identifying import…
Shapley variable importance clouds for interpretable machine learning
Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty +4
Interpretable machine learning has been focusing on explaining final models that optimize performance. The current state-of-the-art is the Shapley additive explanations (SHAP) that…
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Feng Xie, Han Yuan, Yilin Ning +5
Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management. However, c…
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
Han Yuan, Feng Xie, Marcus Eng Hock Ong +7
Background: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among a wide variety of decision-making models for determining the…
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
Feng Xie, Yilin Ning, Han Yuan +4
Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created…