160 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National University of SingaporeSG17 papers
- Duke-NUS Medical SchoolSG16 papers
- Duke UniversityUS13 papers
- SingHealthSG11 papers
- Agency for Science, Technology and ResearchSG6 papers
- Institute of Data ScienceSG6 papers
- Nanyang Technological UniversitySG5 papers
- Institute of High Performance ComputingSG4 papers
- Imperial College LondonGB3 papers
- Institute for Infocomm ResearchSG3 papers
- Columbia UniversityUS2 papers
- Data Storage InstituteSG2 papers
12 papers · 1 filter
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…
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…
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study
Patrick Saux, Pierre Bauvin, Violeta Raverdy +31
Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develo…
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…
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…
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Kaixin Xu, Ziyuan Zhao, Jiapan Gu +5
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle…