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Yilin Ning

Showing results (31-40 of 54) with videos related to

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Journal of Biomedical Informatics|April 14, 2022
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events dataHan Yuan, Feng Xie, Marcus Eng Hock Ong, et al.
Statistical Methods in Medical Research|December 27, 2017
A stratification approach using logit-based models for confounder adjustment in the study of continuous outcomesChuen Seng Tan, Nathalie C Støer, Ying Chen, et al.
JAMA Ophthalmology|November 29, 2023
Topical Atropine for Childhood Myopia Control: The Atropine Treatment Long-Term Assessment StudyYong Li, Michelle Yip, Yilin Ning, et al.
International Journal of Medical Informatics|November 10, 2018
Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workersYing Chen, Shih Ling Kao, Maudrene Tan, et al.
Eclinicalmedicine|March 14, 2022
Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissionsFeng Xie, Nan Liu, Linxuan Yan, et al.
Diabetes Research and Clinical Practice|September 13, 2017
Longitudinal trends in HbA1c and associations with comorbidity and all-cause mortality in Asian patients with type 2 diabetes: A cohort studyMiyang Luo, Wei Yen Lim, Chuen Seng Tan, et al.
Health Data Science|September 8, 2025
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data AnalysisSiqi Li, Xin Li, Kunyu Yu, et al.
Health Data Science|December 5, 2024
Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data AnalysisSiqi Li, Di Miao, Qiming Wu, et al.
BMC Medical Research Methodology|June 8, 2020
Robust estimation of the effect of an exposure on the change in a continuous outcomeYilin Ning, Nathalie C Støer, Peh Joo Ho, et al.
STAR Protocols|May 13, 2023
A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomesFeng Xie, Yilin Ning, Mingxuan Liu, et al.
Pageof 6

Showing results (31-40 of 54) with videos related to

Sort By:
Pageof 6
Journal of Biomedical Informatics|April 14, 2022
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events dataHan Yuan, Feng Xie, Marcus Eng Hock Ong, et al.
Statistical Methods in Medical Research|December 27, 2017
A stratification approach using logit-based models for confounder adjustment in the study of continuous outcomesChuen Seng Tan, Nathalie C Støer, Ying Chen, et al.
JAMA Ophthalmology|November 29, 2023
Topical Atropine for Childhood Myopia Control: The Atropine Treatment Long-Term Assessment StudyYong Li, Michelle Yip, Yilin Ning, et al.
International Journal of Medical Informatics|November 10, 2018
Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workersYing Chen, Shih Ling Kao, Maudrene Tan, et al.
Eclinicalmedicine|March 14, 2022
Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissionsFeng Xie, Nan Liu, Linxuan Yan, et al.
Diabetes Research and Clinical Practice|September 13, 2017
Longitudinal trends in HbA1c and associations with comorbidity and all-cause mortality in Asian patients with type 2 diabetes: A cohort studyMiyang Luo, Wei Yen Lim, Chuen Seng Tan, et al.
Health Data Science|September 8, 2025
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data AnalysisSiqi Li, Xin Li, Kunyu Yu, et al.
Health Data Science|December 5, 2024
Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data AnalysisSiqi Li, Di Miao, Qiming Wu, et al.
BMC Medical Research Methodology|June 8, 2020
Robust estimation of the effect of an exposure on the change in a continuous outcomeYilin Ning, Nathalie C Støer, Peh Joo Ho, et al.
STAR Protocols|May 13, 2023
A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomesFeng Xie, Yilin Ning, Mingxuan Liu, et al.
Pageof 6