Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review
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By
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Shiqiong Yan
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Ping Zhang
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Wanwan Qiao
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Sijia Xie
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Huan Hu
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Yi Gao
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Linli Xie
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Jie Jing
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June 10, 2026
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Clinical Scorecard: Utilizing Machine Learning to Forecast Intraoperative Hemorrhage in Surgical Patients: A Scoping Review
At a Glance
| Category | Detail |
|---|
| Condition | |
| Key Mechanisms | Machine Learning algorithms for predicting bleeding during surgery (source needed). |
| Target Population | |
| Care Setting | |
Key Highlights
- Remove unsupported phrases and ensure all highlights are directly sourced.
Guideline-Based Recommendations
Diagnosis
- Utilize ML algorithms to predict intraoperative bleeding (source needed).
Management
- Focus on optimizing preoperative risk assessment and real-time interventions (source needed).
Monitoring & Follow-up
- Ensure accurate monitoring of estimated blood loss as a quality standard (source needed).
Risks
- Inappropriate transfusion is an independent risk factor for postoperative infection and organ dysfunction (source needed).
Patient & Prescribing Data
ML models can enhance prediction of bleeding risk (source needed).
Clinical Best Practices
- Adopt systematic approaches for evaluating ML methodologies (source needed).
- Standardize validation frameworks for predictive models (source needed).
Related Resources & Content
