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

Preprints

• Jin Y, Xu K, Wang Y, Chu C, Mai X, Zhou Z, Meyer A, Chen Y, Saarunya G, Dudley RA, Ingraham NE, Wacker DA, Lin M, Puskarich MA, Xie F. MultiDiseasePred: An Interpretable Multi-Disease Risk Prediction Framework and Its Application for Early Risk Stratification in the Emergency Department. Research Square. 2026. Preprint.

• Xie F, Xiong X, Mai X, Zhou S, Liu Y, Yu K, Xu X, Lin M, Zhang R, Puskarich M. ED-MERGE: A Dynamic Multimodal Framework for Early Risk Stratification in the Emergency Department with External Validation. Research Square. 2026. Preprint. AMIA 2026 Annual Symposium podium presentation.

• Mai X, Liu Y, Ning Y, Wacker DA, Lin M, Liu N, Puskarich MA, Xie F. FairED: An Open Benchmarking Framework for Reproducible Evaluation of Algorithmic Fairness in Emergency Care Across Clinical Sites. Research Square. 2026. Preprint.

• Yu K, Zhou S, Song Y, Zhan Z, Peng J, Zhou K, Chen T, Xie F, Wang M, Fu H, Lin M, Zhang R. PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities. arXiv:2604.04999. 2026.

• Zhou S, Yu K, Wang S, Xie W, Zhan Z, Tsai MH, Chung YH, Hou S, Zhou H, Zeng M, Ramu B, Chen LY, Xie F, Zhang R. HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology. arXiv:2603.10764. 2026.

• Kong Y, Mai X, Meyer A, Fang Y, Xu Z, Song Z, Gong C, Wacker DA, Heneghan J, Wang Q, Ho AFW, Zhang R, Lin M, Saarunya G, Xu X, Lusczek E, Puskarich MA, Liu N, Celi LA, Xie F. Application of Foundation Models in Emergency and Critical Care: A Scoping Review. Research Square. 2026. Preprint.

• Jin Y, Wang Y, Huang X, Wacker DA, Puskarich MA, Xie F. Development and multicenter external validation of A Data-Driven Scoring System for Early and Rapid Identification of Sepsis in Emergency Departments. medRxiv. 2025. doi: 10.1101/2025.09.27.25336784.

• Mai X, Liu Y, Chung P, Reiss JD, Zhou S, Wong RJ, Lin M, Maric I, Sirota M, Aghaeepour N, Zhang R, Stevenson DK, Xie F. Leveraging Foundation Models in Maternal and Child Health: A Systematic Review. medRxiv. 2025. doi: 10.1101/2025.08.29.25333768.

• Peng J, Zhou S, Yang L, Song Y, Zhang M, Zhou K, Xie F, Lin M, Zhang R, Chen T. Continually evolved multimodal foundation models for cancer prognosis. arXiv:2501.18170. 2025.

Peer-reviewed Publications

• Zhou S, Yu K, Zhan Z, Zhou H, Zeng M, Xie F, Sha Z, Zhang R. Early epilepsy detection from electronic health records with large language models. npj Digital Medicine. 2026. doi: 10.1038/s41746-026-03346-2.

• Payrovnaziri SN, Shome S, Costello J, Reiss JD, Maric I, Seong D, Espinosa C, Phongpreecha T, Xie F, Kim Y, Berson E, Shu CH, Saarunya G, Tan Y, Hazra D, Oskotsky TT, De Francesco D, Becker M, Mohammadi N, Xue L, Shaw GM, Sirota M, Stevenson DK, Prince LS, Aghaeepour N. Dynamic trajectories of neonatal laboratory measurements in the first 26 weeks of life. npj Digital Medicine. 2026. doi: 10.1038/s41746-026-03142-y.

• Hong C, Liew JCK, Yu J, Barry T, Blewer AL, Buckland DM, …, Xie F, …, Ong MEH, Liu N. Building a Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care: Protocol for a FAIR-EC Study. JMIR Research Protocols. 2026;15(1):e74202.

• Kim Y, Marić I, Kashiwagi CM, Han L, Chung P, Reiss JD, …, Xie F, …, Prince LS, Aghaeepour N. PregMedNet: Multifaceted maternal medication impacts on neonatal complications. Nature Communications. 2026. doi: 10.1038/s41467-026-75000-0.

• Gertel ER, Ding VY, Phongpreecha T, Xie F, Parsons A, Marić I, Aghaeepour N, Sylvester KG, Chock VY, Kerner JA, Martin CR, Stevenson DK, Shaw GM, Reiss JD. Triglyceride profiles in preterm infants reveal dynamic and age-specific trajectories. Frontiers in Pediatrics. 2026;14:1777798. doi: 10.3389/fped.2026.1777798.

• Wang Z, Lee JW, Chakraborty T, Ning Y, Liu M, Xie F, Ong MEH, Liu N. Survival Modeling Using Deep Learning, Machine Learning, and Statistical Methods: A Comparative Analysis for Predicting Mortality After Hospital Admission. Health Data Science. 2026;6:0449. doi: 10.34133/hds.0449.

• Myers C, Espinoza CM, Clarke A, Lusczek ER, Xie F, Steffen BT, Nwosu ZC, Trikudanathan G, Saarunya G, Bellin M, Beilman G. Personalizing treatment of pancreatitis-associated chronic pain: the need for an integrated omics approach. Inflammation Research. 2026;75(1):92. doi: 10.1007/s00011-026-02219-4.

• Song Y, Zhan Z, Xie F, Wang N, Peng Y, Zhang R, Lin M. A cancer-type-aware framework for robust multimodal survival prediction under missing modalities. Briefings in Bioinformatics. 2026;27(2):bbag124.

• Roger J, Xie F, Costello JM, Tang AS, Liu J, Oskotsky TT, Woldemariam SR, Kosti I, Le BL, Snyder MP, Giudice LC, Shaw GM, Stevenson DK, Rajkovic A, Glymour MM, Torgerson D, Aghaeepour N, Cakmak H, Lathi RB, Sirota M. Leveraging health records to identify diagnoses associated with recurrent pregnancy loss across two medical centers. iScience. 2026;29(2).

• Chang AL, Reiss JD, Culos A, Becker M, Mayo JA, Marić I, De Francesco D, Phongpreecha T, Espinosa CA, Mataraso SJ, Berson E, Kim Y, Xue L, Xie F, Shu CH, Fallahzadeh R, Bidoki NH, Xenochristou M, Zhang M, Profit J, Lee HC, Gaudillière B, Angst MS, Hawken S, Wilson K, Stevenson DK, Shaw GM, Sylvester KG, Aghaeepour N. Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning. Science Translational Medicine. 2026;18(833):eadv4942. doi: 10.1126/scitranslmed.adv4942.

• Xie F, Chung P, Reiss JD, Tjoa E, De Francesco D, Phongpreecha T, Haberkorn W, Chakraborty D, Chang AL, James T, Kim Y, Mataraso S, Espinosa C, Yang L, Shu CH, Xue L, Berson E, Mohammadi N, Shome S, Reincke SM, Ghanem M, Maric I, Gaudilliere B, Angst MS, Sylvester K, Shaw GM, Prince LS, Stevenson DK, Aghaeepour N. Development and validation of a pre-trained language model for neonatal morbidities: a retrospective, multicentre, prognostic study. The Lancet Digital Health. 2025;7(12).

• Woldemariam SR, Xie F, Roldan A, Roger J, Tang AS, Oskotsky TT, Stevenson DK, Lathi RB, Rajkovic A, Allen IE, Aghaeepour N, Eisenberg M, Sirota M. Leveraging electronic health records from two hospital systems identifies male infertility-associated comorbidities across time. Communications Medicine. 2025;5(1):380. doi: 10.1038/s43856-025-01071-7.

• Phongpreecha T, Ghanem M, Reiss JD, Oskotsky TT, Mataraso SJ, De Francesco D, …, Xie F, …, Prince LS, Aghaeepour N. AI-guided precision parenteral nutrition for neonatal intensive care units. Nature Medicine. 2025;31(6):1882-1894. doi: 10.1038/s41591-025-03601-1.

• Seong D, Mataraso S, Espinosa C, Berson E, Reincke SM, Xue L, Kashiwagi C, Kim Y, Shu CH, Chung P, Ghanem M, Xie F, Wong RJ, Angst MS, Gaudilliere B, Shaw GM, Stevenson DK, Aghaeepour N. Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models. Briefings in Bioinformatics. 2024;25(6):bbae574.

• 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. 2024;2(3):205-237. doi: 10.1002/med4.75

• 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