16 citations · 36 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…