Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review - Report - MDSpire

Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review

  • By

  • Shiqiong Yan

  • Ping Zhang

  • Wanwan Qiao

  • Sijia Xie

  • Huan Hu

  • Yi Gao

  • Linli Xie

  • Jie Jing

  • June 10, 2026

  • 0 min

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Clinical Report: Utilizing Machine Learning to Forecast Intraoperative Hemorrhage

Overview

This scoping review examines the application of machine learning (ML) algorithms in predicting intraoperative hemorrhage across various surgical settings.

Background

Intraoperative bleeding poses significant risks during surgical procedures, contributing to increased mortality and postoperative complications.

Data Highlights

No numerical data available in the source material.

Key Findings

  • ML methods outperform traditional logistic regression models in predicting intraoperative bleeding.
  • Current predictive models exhibit significant variability in methodological quality.
  • Research predominantly focuses on single surgical procedures.
  • Inadequate external validation hinders the clinical translation of ML algorithms.
  • Standardized evaluation frameworks are necessary.

Clinical Implications

Addressing methodological inconsistencies and enhancing model validation are essential for effective implementation in surgical settings.

Conclusion

The review highlights the need for systematic approaches to overcome current research limitations.

Related Resources & Content

  1. Author(s)/Org, Source, Year -- Title
  2. npj Digital Medicine, 2026 -- An Interpretable Machine Learning Approach for Predicting Postoperative Risks and Supporting Surgical Decisions in Cranioplasty
  3. Frontiers in Medicine, 2026 -- Research on predicting risk factors for re-bleeding in the acute phase of intracerebral hemorrhage using machine learning algorithms
  4. Frontiers in Neurology — Machine learning model for unfavorable outcome prediction in neurosurgical patients: the potential role of liver function markers
  5. Guidance on implementing patient blood management to improve global blood health status
  6. Anaemia in the Perioperative Pathway | Centre for Perioperative Care
  7. Patient blood management guideline for adults with critical bleeding | National Blood Authority
  8. Red Blood Cell Transfusion: 2023 AABB International Guidelines | Critical Care Medicine | JAMA | JAMA Network
  9. ACC/AHA Task Force on Clinical Practice Guidelines
  10. Tranexamic Acid in Patients Undergoing Noncardiac Surgery | New England Journal of Medicine
  11. Liberal or Restrictive Postoperative Transfusion in Cardiac Risk
  12. Viscoelastic haemostatic assays to guide therapy in elective surgery: an updated systematic review and meta‐analysis - PMC
  13. Mapping the landscape of machine learning models used for predicting transfusions in surgical procedures: a scoping review | BMC Medical Informatics and Decision Making | Springer Nature Link
  14. Machine Learning in Assessing Intraoperative Blood Loss: A Systematic Review and Meta-Analysis - PubMed
  15. Development and Validation of a Risk Model to Predict Intraoperative Blood Transfusion | Anesthesiology | JAMA Network Open | JAMA Network
  16. Machine learning for the prediction of blood transfusion risk during or after mitral valve surgery: a multicenter retrospective cohort study | Scientific Reports

Original Source(s)

Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review

  • by Shiqiong Yan, Ping Zhang, Wanwan Qiao, Sijia Xie, Huan Hu, Yi Gao, Linli Xie, Jie Jing

  • June 10, 2026

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