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Feng Xie 0004
Person information
- affiliation: Stanford University, School of Medicine, Department of Biomedical Data Science, Stanford, CA, USA
- affiliation (PhD 2022): Duke-NUS Medical School, Singapore
Other persons with the same name
- Feng Xie — disambiguation page
- Feng Xie 0001 — RMIT University, School of Computer Science and IT, Melbourne, Australia
- Feng Xie 0002
— Peking University, School of Mathematical Sciences, Beijing, China (and 1 more)
- Feng Xie 0003
- Feng Xie 0005 — China Information Technology Security Evaluation Center, Beijing, China
- Feng Xie 0006
- Feng Xie 0007 — McMaster University, Department of Computing and Software, Hamilton, Canada
- Feng Xie 0008 — Facebook Reality Labs, USA (and 1 more)
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2020 – today
- 2024
- [i10]Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission. CoRR abs/2403.06999 (2024) - 2023
- [j6]Mingxuan Liu, Siqi Li, Han Yuan
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artif. Intell. Medicine 142: 102587 (2023) - [j5]Siqi Li, Pinyan Liu, Gustavo G. Nascimento, Xinru Wang, Fabio Renato Manzolli Leite, Bibhas Chakraborty, Chuan Hong, Yilin Ning, Feng Xie, Zhen Ling Teo, Daniel Shu Wei Ting, Hamed Haddadi, Marcus Eng Hock Ong, Marco Aurélio Peres, Nan Liu:
Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. J. Am. Medical Informatics Assoc. 30(12): 2041-2049 (2023) - [j4]Siqi Li
FedScore: A privacy-preserving framework for federated scoring system development. J. Biomed. Informatics 146: 104485 (2023) - [c5]Han Yuan, Jin Wee Lee, Mingxuan Liu, Siqi Li, Chenglin Niu, Jun Wen, Feng Xie:
Interpretable Machine Learning-Based Risk Scoring with Individual and Ensemble Model Selection for Clinical Decision Making. Tiny Papers @ ICLR 2023 - [i9]Siqi Li, Yilin Ning
FedScore: A privacy-preserving framework for federated scoring system development. CoRR abs/2303.00282 (2023) - [i8]Siqi Li, Pinyan Liu, Gustavo G. Nascimento, Xinru Wang, Fabio Renato Manzolli Leite, Bibhas Chakraborty, Chuan Hong, Yilin Ning
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review. CoRR abs/2304.07310 (2023) - 2022
- [j3]Feng Xie
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. J. Biomed. Informatics 125: 103959 (2022) - [j2]Feng Xie
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. J. Biomed. Informatics 126: 103980 (2022) - [j1]Han Yuan
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. J. Biomed. Informatics 129: 104072 (2022) - [c4]Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu:
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. AMIA 2022 - [c3]Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu:
AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes. AMIA 2022 - [c2]Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, Marcus Eng Hock Ong, Fei Gao, Nan Liu:
Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records. AMIA 2022 - [i7]Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu:
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. CoRR abs/2201.03291 (2022) - [i6]Seyed Ehsan Saffari, Yilin Ning, Feng Xie, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu:
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes. CoRR abs/2202.08407 (2022) - [i5]Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu:
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. CoRR abs/2210.08258 (2022) - 2021
- [c1]Feng Xie, Bibhas Chakraborty, Nan Liu, Marcus Eng Hock Ong:
Development and Validation of a Survival Score for the Emergency Department in Singapore. AMIA 2021 - [i4]Feng Xie
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. CoRR abs/2106.06957 (2021) - [i3]Han Yuan, Feng Xie, Marcus Eng Hock Ong, Yilin Ning, Marcel Lucas Chee, Seyed Ehsan Saffari, Hairil Rizal Abdullah, Benjamin Alan Goldstein, Bibhas Chakraborty, Nan Liu:
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. CoRR abs/2107.06039 (2021) - [i2]Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu:
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. CoRR abs/2107.09951 (2021) - [i1]Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, Marcus Eng Hock Ong, Fei Gao, Nan Liu:
Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records. CoRR abs/2111.11017 (2021)
Coauthor Index
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