Feng Xie

Feng Xie

Assistant Professor in Computational Health Sciences

You can also find my full publication list on my Google Scholar profile

  • Yuan H*, Yu K*, Xie F*, Liu M, Sun S. Automated Machine Learning (AutoML) with Interpretation: A Systematic Review of Methodologies and Applications in Healthcare. Medicine Advances, In Press

  • Yu JY, Kim D, Yoon S, Kim T, Heo S, Chang H, Han GS, Jeong KW, Park RW, Gwon JM, Xie F, Ong MEH, Ng YY, Joo HJ, Cha WC. Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model. Sci Rep. 2024 Mar 20;14(1):6666. doi: 10.1038/s41598-024-54364-7.

  • Ghanem M, Espinosa C, Chung P, Reincke M, Harrison N, Phongpreecha T, Shome S, Saarunya G, Berson E, James T, Xie F, Shu CH, Hazra D, Mataraso S, Kim Y, Seong D, Chakraborty D, Studer M, Xue L, Marić I, Chang AL, Tjoa E, Gaudillière B, Tawfik VL, Mackey S, Aghaeepour N. Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator’s publication trends. Heliyon. 2024 Apr 3;10(7):e29050. doi: 10.1016/j.heliyon.2024.e29050.

  • Phongpreecha T, Mathi K, Cholerton B, Fox EJ, Sigal N, Espinosa C, Reincke M, Chung P, Hwang LJ, Gajera CR, Berson E, Perna A, Xie F, Shu CH, Hazra D, Channappa D, Dunn JE, Kipp LB, Poston KL, Montine KS, Maecker HT, Aghaeepour N, Montine TJ. Single-cell peripheral immunoprofiling of lewy body and Parkinson’s disease in a multi-site cohort. Mol Neurodegener. 2024 Aug 1;19(1):59. doi: 10.1186/s13024-024-00748-2.

  • 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 https://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: https://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. https://www.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

  • 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. (https://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.

  • 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. https://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). https://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.

  • Rajendram MF, Zarisfi F, Xie F, Shahidah N, Pek PP, Yeo JW, Tan BY, Ma M, Do Shin S, Tanaka H, Ong MEH, Liu N, Ho AFW. 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. Frontiers in Medicine. 2022 Sep 8;9:930226. doi: 10.3389/fmed.2022.930226.

  • 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

Media Highlight: https://healthitanalytics.com/news/machine-learning-triage-tool-better-predicts-mortality-in-ed and https://specialty.mims.com/topic/new-tool-outdoes-existing-triage-scores-at-estimating-mortality-risk-in-ed

  • 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

  • Liu N, Guo D, Koh ZX, Ho AFW, Xie F, Tagami T, Sakamoto JT, Pek PP, Chakraborty B, Lim SH, Tan JWC, Ong MEH. Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department. BMC Cardiovasc Disorder. 2020;20(1):168. doi: 10.1186/s12872-020-01455-8

  • 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