SynthesisInternational nursing review2026
Machine Learning in Assessing Intraoperative Blood Loss: A Systematic Review and Meta-Analysis.
Synthesis in International nursing review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
aimTo evaluate the value of machine learning in assessing intraoperative blood loss by comparing associated outcomes with those of the gold standard.
backgroundIntraoperative bleeding is a leading cause of death in surgical patients and may be preventable through early and accurate assessment of blood loss. Machine learning models are used for measuring intraoperative hemorrhage with conventional assessment methods. However, outcome metrics vary across studies.
methodsA systematic review and meta-analysis. Data were retrieved from Web of Science, PubMed, Embase, Cochrane Library, and CINAHL, with searches conducted through August 18, 2025.
resultsTwelve studies were included. The pooled correlation coefficient between machine learning models and the gold standard for assessing intraoperative blood loss was high. DISCUSSION: Machine learning models demonstrate high accuracy and reliability in assessing intraoperative blood loss. Heterogeneity was high, likely attributable to differences in publication year, country, study subjects, sample type, and modeling method.
conclusionModels should be promoted for clinical use to improve blood loss assessment accuracy and to potentially reduce perioperative risk. IMPLICATIONS FOR NURSING: Novel machine learning models could enhance the accuracy and applicability of existing models, providing nursing staff with a more efficient tool for assessing blood loss. This will optimize the nursing decision-making process, reduce adverse events caused by underestimating or overestimating blood loss, and improve patient safety. IMPLICATIONS FOR NURSING POLICY: We provide a reference for exploring the application of artificial intelligence in other nursing fields, promoting interdisciplinary research and driving continuous innovation and progress in nursing.
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