Search Results for author:
Found 18 papers, 10 papers with code
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness.
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States.
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
The calibration of DeepSurv (IBS: 0. 041) performed the best, followed by RSF (IBS: 0. 042) and GBM (IBS: 0. 0421), all using the full variables.
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare, but ethical discussions are yet to translate into operationalisable solutions.
Towards clinical AI fairness: A translational perspective
In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness.
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice.
A roadmap to fair and trustworthy prediction model validation in healthcare
A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear.
FedScore: A privacy-preserving framework for federated scoring system development
We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models.
Shapley variable importance cloud for machine learning models
Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e. g., by using the Shapley additive explanations (SHAP) method.
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
We found that model backbone(s) differed among data types as well as the imputation strategy.
Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making
This study sought to investigate the effects of data imbalance on SHAP explanations for deep learning models, and to propose a strategy to mitigate these effects.
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes
This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes.
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
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models.
Shapley variable importance clouds for interpretable machine learning
Interpretable machine learning has been focusing on explaining final models that optimize performance.
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
To some extent, current deep learning solutions can address these challenges.
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
