research
∙
04/26/2023
Towards clinical AI fairness: A translational perspective
Artificial intelligence (AI) has demonstrated the ability to extract ins...
share
research
∙
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...
research
∙
04/07/2023
A roadmap to fair and trustworthy prediction model validation in healthcare
A prediction model is most useful if it generalizes beyond the developme...
research
∙
03/01/2023
FedScore: A privacy-preserving framework for federated scoring system development
We propose FedScore, a privacy-preserving federated learning framework f...
research
∙
12/16/2022
Shapley variable importance cloud for machine learning models
Current practice in interpretable machine learning often focuses on expl...
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
∙
02/17/2022
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes
Background: Risk prediction models are useful tools in clinical decision...
research
∙
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...
research
∙
10/06/2021
Shapley variable importance clouds for interpretable machine learning
Interpretable machine learning has been focusing on explaining final mod...
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