Feng Xie

Orcid: 0000-0002-0215-667X

Affiliations:
  • Stanford University, School of Medicine, Department of Biomedical Data Science, Stanford, CA, USA
  • Duke-NUS Medical School, Singapore (PhD 2022)


According to our database1, Feng Xie authored at least 14 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission.
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

FedScore: A privacy-preserving framework for federated scoring system development.
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

Interpretable Machine Learning-Based Risk Scoring with Individual and Ensemble Model Selection for Clinical Decision Making.
Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

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

Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records.
Proceedings of the AMIA 2022, 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
Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records.
CoRR, 2021

Development and Validation of a Survival Score for the Emergency Department in Singapore.
Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021


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