160 citations · 523 across the 10 of their papers we have counts for
10 papers
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 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…
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…
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…