Objective:

To explore the use of machine learning algorithms in predicting intraoperative bleeding across various surgical settings and to evaluate the impact of model-building methods on prediction outcomes.

Approach:

Key Findings:

• Current predictive tools for intraoperative bleeding are limited by subjective assessment methods and calculation-based inaccuracies.
• Traditional prediction models like logistic regression are constrained by linear assumptions, while machine learning methods show improved predictive accuracy.
• Existing research on ML for intraoperative bleeding is fragmented, focusing on single procedures with variable methodological quality.

Interpretation:

Limitations:

• The review may not encompass all relevant studies due to the search strategy and eligibility criteria.
• Methodological limitations in existing studies, such as inconsistent data preprocessing and lack of robust external validation.

Conclusion:

The integration and assessment of machine learning methodologies in intraoperative bleeding prediction require systematic approaches to address current fragmentation and methodological concerns.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.