148 citations · 335 across the 16 of their papers we have counts for
16 papers
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
Siqi Li, Chuan Hong, Ziye Tian +9
Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target…
Communication-efficient distributed hazard difference estimation for heterogeneous multi-site survival data
Ziwen Wang, Siqi Li, Marcus Eng Hock Ong +1
Multi-site collaboration can power survival models that no single hospital could fit alone, but privacy rules and protected computing environments block patient-level data sharing…
TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk
Mengying Yan, Ziye Tian, Siqi Li +4
Clinical decision support tools built on electronic health records often experience performance drift due to temporal population shifts, particularly when changes in the clinical e…
Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value
Wangxuan Fan, Ching Wang, Siqi Li +1
For many real-world applications, understanding feature-outcome relationships is as crucial as achieving high predictive accuracy. While traditional neural networks excel at predic…
Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity
Siqi Li, Molei Liu, Yiwei Lyu +3
Machine learning models optimized for average performance can perform poorly on vulnerable subpopulations. Existing approaches often rely on groups specified in advance, yet fairne…
Toward Fair Federated Learning under Demographic Disparities and Data Imbalance
Qiming Wu, Siqi Li, Doudou Zhou +1
Ensuring fairness is critical when applying artificial intelligence to high-stakes domains such as healthcare, where predictive models trained on imbalanced and demographically ske…
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
Wangxuan Fan, Siqi Li, Doudou Zhou +4
Explainable artificial intelligence (XAI) is essential for trustworthy machine learning (ML), particularly in high-stakes domains such as healthcare and finance. Shapley value (SV)…
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…
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…
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…
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…
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…
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…
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…
Benchmarking emergency department triage prediction models with machine learning and large public electronic health records
Feng Xie, Jun Zhou, Jin Wee Lee +10
The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become…