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Cited by 8

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study

Deng, Fuxing, Cao, Yaoyuan, Wang, Hui, Zhao, Shuangping·2025·Journal of Critical Care
DOI 10.1016/j.jcrc.2024.154923
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Research works printed but not linked: 29

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  3. Kanwal, 2010, Measuring quality of Care in Patients with nonvariceal upper gastrointestinal hemorrhage: development of an explicit quality Indicator set, Am J Gastroenterol, 105, 1710, 10.1038/ajg.2010.180 · DOI
  4. Chaudhary, 2021, Upper gastrointestinal bleeding in Scotland 2000-2015: trends in demographics, Aetiology and outcomes, Aliment Pharmacol Ther, 53, 383, 10.1111/apt.16170 · DOI
  5. Barkun, 2010, International consensus recommendations on the Management of Patients with nonvariceal upper gastrointestinal bleeding, Ann Intern Med, 152, 101, 10.7326/0003-4819-152-2-201001190-00009 · DOI · Paper page
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  11. Liu, 2022, Detecting and measuring depression on social media using a machine learning approach: systematic review, JMIR Mental Health, 9, 10.2196/27244 · DOI
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  17. Deyo, 1992, Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases, J Clin Epidemiol, 45, 613, 10.1016/0895-4356(92)90133-8 · DOI · Paper page
  18. Blasi, 2020, Mixed fibrinolytic phenotypes in decompensated cirrhosis and acute-on-chronic liver failure with Hypofibrinolysis in those with complications and poor survival, Hepatol, 71, 1381, 10.1002/hep.30915 · doi:10.1002/hep.30915
  19. Simonyan, 2013, Deep inside convolutional networks: Visualising image classification models and saliency maps, CoRR abs/1312.6034 · arXiv
  20. Kang, 2022, Machine learning model for the prediction of hemorrhage in intensive care units, Healthc Inform Res, 28, 364, 10.4258/hir.2022.28.4.364 · DOI
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Metadata

Authors
Deng, F., Cao, Y., Wang, H., & Zhao, S.
Venue
Journal of Critical Care
Publisher
Elsevier BV
Published
2025-02
Volume
85
Pages
154923
ISSN
0883-9441
DOI
10.1016/j.jcrc.2024.154923
Cited by
8
References
3
Language
en
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CoChat. (2026). Citation check: Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study. https://app.cochat.ai/papers/10.1016/j.jcrc.2024.154923
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Deng, F., Cao, Y., Wang, H., & Zhao, S. (2025). Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study. Journal of Critical Care. https://doi.org/10.1016/j.jcrc.2024.154923

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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