Yilin Ning
Orcid: 0000-0002-6758-4472
According to our database1,
Yilin Ning
authored at least 31 papers
between 2016 and 2024.
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Bibliography
2024
FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare.
Patterns, 2024
Disparities in clinical studies of AI enabled applications from a global perspective.
npj Digit. Medicine, 2024
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction.
CoRR, 2024
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis.
CoRR, 2024
CoRR, 2024
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission.
CoRR, 2024
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare.
CoRR, 2024
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data.
CoRR, 2024
2023
Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.
J. Am. Medical Informatics Assoc., November, 2023
J. Biomed. Informatics, October, 2023
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.
Artif. Intell. Medicine, August, 2023
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches.
CoRR, 2023
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist.
CoRR, 2023
CoRR, 2023
2022
Multiscale Bidirectional Diversity Entropy for Diesel Injector Fault-Type Diagnosis and Fault Degree Diagnosis.
IEEE Trans. Instrum. Meas., 2022
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.
J. Biomed. Informatics, 2022
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies.
J. Biomed. Informatics, 2022
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.
J. Biomed. Informatics, 2022
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.
CoRR, 2022
Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making.
CoRR, 2022
AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes.
Proceedings of the AMIA 2022, 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 Study.
Proceedings of the AMIA 2022, 2022
2021
2019
Proceedings of the 6th International Conference on Dependable Systems and Their Applications, 2019
2018
Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workers.
Int. J. Medical Informatics, 2018
2016
Proceedings of the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics, 2016