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04/28/2023
Identification and Estimation of Causal Effects Using non-Gaussianity and Auxiliary Covariates
Assessing causal effects in the presence of unmeasured confounding is a ...
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04/28/2023
Improving Knowledge Graph Entity Alignment with Graph Augmentation
Entity alignment (EA) which links equivalent entities across different k...
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04/14/2023
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Federated learning (FL) has gained popularity in clinical research in re...
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03/01/2023
Multi-Arm Robot Task Planning for Fruit Harvesting Using Multi-Agent Reinforcement Learning
The emergence of harvesting robotics offers a promising solution to the ...
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03/01/2023
FedScore: A privacy-preserving framework for federated scoring system development
We propose FedScore, a privacy-preserving federated learning framework f...
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10/15/2022
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Objective: The proper handling of missing values is critical to deliveri...
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01/10/2022
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort
Risk scores are widely used for clinical decision making and commonly ge...
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11/22/2021
Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records
There is a continuously growing demand for emergency department (ED) ser...
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10/17/2021
Real Time Cluster Path Tracing
Photorealistic rendering effects are common in films, but most real time...
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07/21/2021
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Objective: Temporal electronic health records (EHRs) can be a wealth of ...
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07/13/2021
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
Background: Medical decision-making impacts both individual and public h...
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06/13/2021
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
Scoring systems are highly interpretable and widely used to evaluate tim...
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10/10/2020
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs
Causal discovery aims to recover causal structures or models underlying ...
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09/19/2020
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Discovering causal structures among latent factors from observed data is...
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03/08/2019
