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Explainable machine learning predicts mortality in critically ill patients with nonvariceal upper gastrointestinal bleeding: a MIMIC-IV study with external validation

Lu, Jialin, Yu, Chuting, Li, Hangbang, Li, Qiuxin, Gao, Ye, Wang, Wei, Wang, Luowei·2026·BMC Gastroenterology
DOI 10.1186/s12876-026-04709-5

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Research works printed but not linked: 24

  1. JAIRATH J V, et al. Why do mortality rates for nonvariceal upper gastrointestinal bleeding differ around the world? A systematic review of cohort studies. Can J Gastroenterol. 2012;26(8):537–43. https://doi.org/10.1155/2012/862905. · DOI
  2. LASSEN A, et al. Complicated and uncomplicated peptic ulcers in a Danish county 1993–2002: a population-based cohort study [J]. Am J Gastroenterol. 2006;101(5):945–53. https://doi.org/10.1111/j.1572-0241.2006.00518.x. · DOI · Paper page
  3. TARGOWNIK L E, NABALAMBA A. Trends in management and outcomes of acute nonvariceal upper gastrointestinal bleeding: 1993–2003 [J]. Clin Gastroenterol Hepatol, 2006, 4(12): 1459–66, https://doi.org/10.1016/j.cgh.2006.08.018 · DOI · Paper page
  4. VAN LEERDAM M E, et al. Acute upper GI bleeding: did anything change? Time trend analysis of incidence and outcome of acute upper GI bleeding between 1993/1994 and 2000 [J]. Am J Gastroenterol. 2003;98(7):1494–9. https://doi.org/10.1111/j.1572-0241.2003.07517.x. · DOI · Paper page
  5. KHAMAYSI I, GRALNEK I M. Acute upper gastrointestinal bleeding (UGIB) - initial evaluation and management [J]. Best Pract Res Clin Gastroenterol. 2013;27(5):633–8. https://doi.org/10.1016/j.bpg.2013.09.002. · doi:10.1016/j.bpg.2013.09.002
  6. LANAS A, et al. Non-variceal upper gastrointestinal bleeding [J]. Nat Reviews Disease Primers. 2018;4(1). https://doi.org/10.1038/nrdp.2018.20. · DOI · Paper page
  7. SUNG J J Y, et al. Causes of Mortality in Patients With Peptic Ulcer Bleeding: A Prospective Cohort Study of 10,428 Cases [J]. Am J Gastroenterol, 2010, 105(1): 84–9, https://doi.org/10.1038/ajg.2009.507 · doi:10.1038/ajg.2009.507
  8. KANWAL F, et al. Measuring quality of care in patients with nonvariceal upper gastrointestinal hemorrhage: development of an explicit quality indicator set [J]. Am J Gastroenterol. 2010;105(8):1710–8. https://doi.org/10.1038/ajg.2010.180. · DOI
  9. ROTONDANO G. Epidemiology and diagnosis of acute nonvariceal upper gastrointestinal bleeding [J]. Gastroenterol Clin North Am, 2014, 43(4): 643–63, https://doi.org/10.1016/j.gtc.2014.08.001 · doi:10.1016/j.gtc.2014.08.001
  10. TIELLEMAN T, et al. Epidemiology and Risk Factors for Upper Gastrointestinal Bleeding [J]. Gastrointest Endosc Clin N Am. 2015;25(3):415–28. https://doi.org/10.1016/j.giec.2015.02.010. · DOI · Paper page
  11. EBRAHIMI BAKHTAVAR H, et al. Clinical Scoring Systems in Predicting the Outcome of Acute Upper Gastrointestinal Bleeding; a Narrative Review [J]. Emerg (Tehran). 2017;5(1):e36.
  12. MARMO R, et al. Mortality from nonulcer bleeding is similar to that of ulcer bleeding in high-risk patients with nonvariceal hemorrhage: a prospective database study in Italy [J]. Gastrointest Endosc. 2012;75(2). https://doi.org/10.1016/j.gie.2011.07.066. 263 – 72, 72.e1. · doi:10.1016/j.gie.2011.07.066
  13. MARMO R, et al. Predicting mortality in non-variceal upper gastrointestinal bleeders: validation of the Italian PNED Score and Prospective Comparison with the Rockall Score [J]. Am J Gastroenterol. 2010;105(6):1284–91. https://doi.org/10.1038/ajg.2009.687. · DOI · Paper page
  14. MARMO R, et al. Predicting mortality in patients with in-hospital nonvariceal upper GI bleeding: a prospective, multicenter database study [J]. Gastrointest Endosc. 2014;79(5):741–e91. https://doi.org/10.1016/j.gie.2013.10.009. · doi:10.1016/j.gie.2013.10.009
  15. ROTONDANO G et al. Artificial neural networks accurately predict mortality in patients with nonvariceal upper GI bleeding [J]. Gastrointest Endosc, 2011, 73(2): 218 – 26, 26.e1-2. https://doi.org/10.1016/j.gie.2010.10.006 · DOI
  16. DIAO Y, et al. Joint learning-based feature reconstruction and enhanced network for incomplete multi-modal brain tumor segmentation [J]. Comput Biol Med. 2023;163:107234. https://doi.org/10.1016/j.compbiomed.2023.107234. · DOI
  17. DENG F, et al. Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study [J]. J Crit Care. 2025;85. https://doi.org/10.1016/j.jcrc.2024.154923. · DOI · Paper page
  18. SHUNG D, et al. Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review [J]. Dig Dis Sci. 2019;64(8):2078–87. https://doi.org/10.1007/s10620-019-05645-z. · DOI
  19. POLLARD TJ, et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research [J]. Sci Data. 2018;5(1):180178. https://doi.org/10.1038/sdata.2018.178. · DOI · Paper page
  20. BLAZEK K, et al. A practical guide to multiple imputation of missing data in nephrology [J]. Kidney Int. 2021;99(1):68–74. https://doi.org/10.1016/j.kint.2020.07.035. · DOI · Paper page
  21. LIANG W, et al. Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19 [J]. JAMA Intern Med. 2020;180(8). https://doi.org/10.1001/jamainternmed.2020.2033. · DOI · Paper page
  22. FRIEDMAN J, et al. Regularization Paths for Generalized Linear Models via Coordinate Descent [J]. J Stat Softw. 2010;33(1):1–22. · DOI · Paper page
  23. DESHMUKH F, MERCHANT SS. Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit [J]. Am J Gastroenterol. 2020;115(10):1657–68. https://doi.org/10.14309/ajg.0000000000000632. · DOI
  24. KIM MS, et al. AIMS65 scoring system is comparable to Glasgow-Blatchford score or Rockall score for prediction of clinical outcomes for non-variceal upper gastrointestinal bleeding [J]. BMC Gastroenterol. 2019;19(1):136. https://doi.org/10.1186/s12876-019-1051-8. · DOI

Metadata

Authors
Lu, J., Yu, C., Li, H., Li, Q., Gao, Y., Wang, W., & Wang, L.
Venue
BMC Gastroenterology
Publisher
Springer Science and Business Media LLC
Published
2026-03-02
Volume
26
Issue
1
ISSN
1471-230X
DOI
10.1186/s12876-026-04709-5
Cited by
1
References
3
Language
en
Licence
Funders
Shanghai Municipal Health Commission (202240357)

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CoChat. (2026). Citation check: Explainable machine learning predicts mortality in critically ill patients with nonvariceal upper gastrointestinal bleeding: a MIMIC-IV study with external validation. https://app.cochat.ai/papers/10.1186/s12876-026-04709-5
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Lu, J., Yu, C., Li, H., Li, Q., Gao, Y., Wang, W., & Wang, L. (2026). Explainable machine learning predicts mortality in critically ill patients with nonvariceal upper gastrointestinal bleeding: a MIMIC-IV study with external validation. BMC Gastroenterology. https://doi.org/10.1186/s12876-026-04709-5

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.