ArticleiScience2024
Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Letter to the Editor: Predicting intraoperative blood loss risk in severe lumbar disc herniation patients undergoing PLIF: a multicenter cohort study using ensemble learning.International journal of surgery (London, England) · 2026Article
- Artificial intelligence in oncology drug development and management: a precision medicine perspective.Frontiers in oncology · 2025Review
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Authors and funding
33 authors.
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Abstract
Lumbar disc herniation (LDH) is a common cause of lower back pain and sciatica, and posterior lumbar interbody fusion (PLIF) is always employed. This multicenter retrospective study investigates predicting intraoperative blood transfusion for LDH patients undergoing PLIF in China. The research includes 6,241 patients from 22 medical centers and employs 8 feature selection methods and 10 machine learning models, including an integrated stacking model. The optimal predictive model was selected based on the receiver operating characteristic area under the curve, clinical applicability, and computational efficiency. Among the evaluated combinations, the simulated annealing support vector machine recursive + stacking model achieved the highest performance with an area under the curve of 0.884, supported by robust calibration and decision curve analyses. A publicly accessible web calculator was developed to assist clinicians in decision-making. This work significantly enhances intraoperative transfusion predictions, providing valuable tools for improving patient management.
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