160 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National University of SingaporeSG12 papers
- Duke-NUS Medical SchoolSG6 papers
- Duke UniversityUS6 papers
- Singapore General HospitalSG6 papers
- SingHealthSG6 papers
- Maastricht UniversityNL2 papers
- Monash UniversityAU2 papers
- SingHealth Duke-NUS Academic Medical CentreSG2 papers
- University of LeedsGB2 papers
- Agency for Science, Technology and ResearchSG1 paper
- Beth Israel Deaconess Medical CenterUS1 paper
- City University of Hong KongHK1 paper
17 papers
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity
Shixiong Wang, Haowei Wang, Xinke Li +1
Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions. Typical treatment frameworks includ…
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…
Model-Based and Graph-Based Priors for Group Testing
Ivan Lau, Jonathan Scarlett, Yang Sun
The goal of the group testing problem is to identify a set of defective items within a larger set of items, using suitably-designed tests whose outcomes indicate whether any defect…
Speaker Extraction with Co-Speech Gestures Cue
Zexu Pan, Xinyuan Qian, Haizhou Li
Speaker extraction seeks to extract the clean speech of a target speaker from a multi-talker mixture speech. There have been studies to use a pre-recorded speech sample or face ima…
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…
Muse: Multi-modal target speaker extraction with visual cues
Zexu Pan, Ruijie Tao, Chenglin Xu +1
Speaker extraction algorithm relies on the speech sample from the target speaker as the reference point to focus its attention. Such a reference speech is typically pre-recorded. O…
On the All-Or-Nothing Behavior of Bernoulli Group Testing
Lan V. Truong, Matthew Aldridge, Jonathan Scarlett
In this paper, we study the problem of non-adaptive group testing, in which one seeks to identify which items are defective given a set of suitably-designed tests whose outcomes in…
LUCE: A Blockchain Solution for monitoring data License accoUntability and CompliancE
Andine Havelange, Michel Dumontier, Birgit Wouters +4
In this paper we present our preliminary work on monitoring data License accoUntability and CompliancE (LUCE). LUCE is a blockchain platform solution designed to stimulate data sha…
Group Testing: An Information Theory Perspective
Matthew Aldridge, Oliver Johnson, Jonathan Scarlett
The group testing problem concerns discovering a small number of defective items within a large population by performing tests on pools of items. A test is positive if the pool con…
Analyzing Partitioned FAIR Health Data Responsibly
Chang Sun, Lianne Ippel, Birgit Wouters +11
It is widely anticipated that the use of health-related big data will enable further understanding and improvements in human health and wellbeing. Our current project, funded throu…
Learning to Detect Fake Face Images in the Wild
Chih-Chung Hsu, Chia-Yen Lee, Yi-Xiu Zhuang
Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns. For example, GAN can be used t…
Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked Data
Tobias Kuhn, Albert Meroño-Peñuela, Alexander Malic +12
Nanopublications are a Linked Data format for scholarly data publishing that has received considerable uptake in the last few years. In contrast to the common Linked Data publishin…
On Reasoning with RDF Statements about Statements using Singleton Property Triples
Vinh Nguyen, Olivier Bodenreider, Krishnaprasad Thirunarayan +6
The Singleton Property (SP) approach has been proposed for representing and querying metadata about RDF triples such as provenance, time, location, and evidence. In this approach,…