F. Xie

发表

Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department

B. Chakraborty, P. Pek, M. Ong, 2020, BMC Cardiovascular Disorders.

Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department

B. Chakraborty, P. Pek, M. Ong, 2019, bioRxiv.

Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department

M. Ong, Nan Liu, L. Choong, 2022, Scientific Reports.

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

F. Siddiqui, M. Ong, Nan Liu, 2021, JMIR research protocols.

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Wynne Hsu, Marcus Eng Hock Ong, Bibhas Chakraborty, 2021, J. Biomed. Informatics.

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques

Marcus Eng Hock Ong, S. Saffari, Nan Liu, 2022, Artif. Intell. Medicine.

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department

M. Ong, Y. Ng, Wonchul Cha, 2022, Scientific Reports.

External validation of the Survival After ROSC in Cardiac Arrest (SARICA) score for predicting survival after return of spontaneous circulation using multinational pan-asian cohorts

P. Pek, M. Ong, Nan Liu, 2022, Frontiers in Medicine.

Benchmarking emergency department prediction models with machine learning and public electronic health records

Marcel Lucas Chee, Logasan SO Rajnthern, Marcus Eng Hock Ong, 2022, Scientific Data.

A predictive score for progression of COVID-19 in hospitalized persons: a cohort study

H. Shan, R. Gale, J. Xia, 2021, npj Primary Care Respiratory Medicine.

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records

Bibhas Chakraborty, Feng Xie, Marcus Eng Hock Ong, 2020, JMIR medical informatics.

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data pdf

Hairil Rizal Abdullah, Marcus Eng Hock Ong, Bibhas Chakraborty, 2021, J. Biomed. Informatics.

Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions

M. Ong, L. Low, Y. Kwan, 2022, EClinicalMedicine.

AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes

M. Ong, S. Saffari, Nan Liu, 2022, BMC Medical Research Methodology.

Federated and distributed learning applications for electronic health records and structured medical data: A scoping review

Nan Liu, Yilin Ning, Pinyan Liu, 2023, J. Am. Medical Informatics Assoc..

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

Daniel M. Buckland, Nan Liu, Yilin Ning, 2023, J. Biomed. Informatics.

Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS)

Se Uk Lee, W. Cha, K. Kajino, 2023, The Lancet regional health. Western Pacific.

Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study

Sean Shao Wei Lam, B. Chakraborty, D. Matchar, 2019, BMJ Open.

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data pdf

Marcus Eng Hock Ong, Bibhas Chakraborty, Yilin Ning, 2021, J. Biomed. Informatics.

Prediction Model for Detection of Sporadic Pancreatic Cancer (PRO-TECT) in a Population-Based Cohort Using Machine Learning and Further Validation in a Prospective Study

T. Luong, J. Pisegna, W. Chen, 2022, medRxiv.

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 pdf

Marcus Eng Hock Ong, Daniel Shu Wei Ting, Yilin Ning, 2022, AMIA.

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions

D. Matchar, M. Ong, B. Goldstein, 2021, JAMA network open.

AKI Risk Score (AKI-RiSc): Developing an Interpretable Clinical Score for Early Identification of Acute Kidney Injury at the Emergency Department

M. Ong, Nan Liu, L. Choong, 2021 .

AKI Risk Score (AKI-RiSc): Developing an Interpretable Clinical Score for Early Identification of Acute Kidney Injury for Patients Presenting to the Emergency Department

N. Liu, A. Ho, F. Xie, 2021, medRxiv.

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records

B. Chakraborty, M. Ong, B. Goldstein, 2020, JMIR medical informatics.

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

Yilin Ning, F. Xie, Mingxuan Liu, 2024, ArXiv.

Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records

Logasan SO Rajnthern, M. Ong, Nan Liu, 2021, AMIA.

Score for Emergency Risk Prediction (SERP): An Interpretable Machine Learning AutoScore-Derived Triage Tool for Predicting Mortality after Emergency Admissions

B. Chakraborty, D. Matchar, N. Liu, 2021, medRxiv.

Benchmarking Predictive Risk Models for Emergency Departments with Large Public Electronic Health Records pdf

Nan Liu, Marcus Eng Hock Ong, An-Kwok Ian Wong, 2021, ArXiv.