Feng Xie, PhD

A complete and up-to-date list of publications is available on the Google Scholar profile.

• Xie F, Ning Y, Liu M, Li S, Saffari SE, Yuan H, Volovici V, Ting DSW, Goldstein BA, Ong MEH, Vaughan R, Chakraborty B, Liu N. A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols.2023;4(2):102302.

With open-sourced software package: cran.r-project.org/web/packages/AutoScore/index.html

Yu JY, Heo S, Xie F, Liu N, Yoon SY, Ong MEH, Ng YY, et al. 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). The Lancet Regional Health - Western Pacific. 2023, 100733

Liu M, Li S, Yuan H, Ong MEH, Ning Y, Xie F, et al. Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial Intelligence in Medicine. 2023. doi.org/10.1016/j.artmed.2023.102587

Li S, Liu P, Nascimento GG, Wang X, Leite FRM, Chakraborty B, Hong C, Ning Y, Xie F, Teo ZL et al: Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. Journal of the American Medical Informatics Association. 2023:ocad170.

Li S, Ning Y, Ong MEH, Chakraborty B, Hong C, Xie F, Yuan H, Liu M, Buckland DM, Chen Y et al: FedScore: A privacy-preserving framework for federated scoring system development. Journal of Biomedical Informatics. 2023, 146:104485.

Xie F, Zhou J, Lee JW, Tan M, Li S, Rajnthern LS, Chee ML, Chakraborty B, Wong AI, Dagan A, Ong MEH, Gao F, Liu N. Benchmarking Risk Triage Models for Emergency Department with Large Public Electronic Health Records. Scientific Data. 2022; 9:658. nature.com/articles/s41597-022-01782-9

Xie F, Liu N, Yan L, Ning Y, Lim KK, Gong C, Kwan YH, Ho AFW, Low LL, Chakraborty B, Ong MEH. Development and Validation of an Interpretable Machine Learning Scoring Tool for Estimating Time to Emergency Readmissions. EClinicalMedicine. 2022; 45:101315 thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00045-1/fulltext#%20

Xie F, Yuan H, Ning Y, Ong MEH, Feng M, Hsu W, Chakraborty B, Liu N. Deep Learning for Temporal Data Representation in Electronic Health Records: A Systematic Review of Challenges and Methodologies. Journal of Biomedical Informatics. 2022; 126:103980. doi.org/10.1016/j.jbi.2021.103980

Xie F, Ning Y, Yuan H, Goldstein BA, Ong MEH, Liu N, Chakraborty B: AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics. 2022; 125:103959. doi.org/10.1016/j.jbi.2021.103959

Liu N, Xie F, Siddiqui FJ, Ho AFW, Chakraborty B, Nadarajan GD, Tan KBK, Ong MEH. Leveraging Large-scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation. JMIR Research Protocols. 2022;11(3):e34201. doi: 10.2196/34201

Yuan H, Xie F, Ong MEH, Ning Y, Chee ML, Saffari SE, Abdullah HR, Goldstein BA, Chakraborty B, Liu N. AutoScore-Imbalance: An Automated Machine Learning Tool to Handle Data Imbalance in Interpretable Clinical Score Development. Journal of Biomedical Informatics. 2022;129:104072. doi:10.1016/j.jbi.2022.104072

Ning Y, Li S, Ong MEH, Xie F, Chakraborty B, Goldstein BA, Ting DSW, Vaughan R, Liu N. 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. PLOS Digit Health 1(6): e0000062. doi.org/10.1371/journal.pdig.0000062

Ang Y, Li S, Ong MEH, Xie F, Teo SH, Choong L, Koniman R, Chakraborty B, Ho AFW, Liu N. Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Scientific Reports. 2022;12(1):7111. doi:10.1038/s41598-022-11129-4

Saffari SE, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong MEH, Liu N. AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes. BMC Medical Research Methodology. 22, 286 (2022). doi.org/10.1186/s12874-022-01770-y

Yu JY, Xie F, Nan L, Yoon S, Ong MEH, Ng YY, Cha WC. An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department. Scientific Report. 2022 Oct 19;12(1):17466. doi: 10.1038/s41598-022-22233-w.

Xie F, Ong MEH, Liew JNMH, Tan KBK, Ho AFW, Nadarajan GD, Low LL, Kwan YH, Goldstein BA, Chakraborty B, and Liu N. Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions. JAMA Network Open. 2021;4(8):e2118467. doi: 10.1001/jamanetworkopen.2021.18467

Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records. JMIR Med Inform. 2020;8(10):e21798. doi: 10.2196/21798

Xie F, Liu N, Wu SX, Ang Y, Low LL, Ho AFW, Lam SSW, Matchar DB, Ong MEH, Chakraborty B. Novel Model for Predicting Inpatient Mortality After Emergency Admission to Hospital in Singapore: Retrospective Observational Study. BMJ Open. 2019;9:e031382. doi: 10.1136/bmjopen-2019-031382