research
∙
03/01/2023
FedScore: A privacy-preserving framework for federated scoring system development
We propose FedScore, a privacy-preserving federated learning framework f...
share
research
∙
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...
research
∙
06/08/2022
Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making
Objective: Shapley additive explanations (SHAP) is a popular post-hoc te...
research
∙
04/24/2022
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models
Nowadays, the interpretation of why a machine learning (ML) model makes ...
research
∙
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 ...
research
∙
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...
research
∙
06/13/2021
