ArticleWorld journal of surgery2023
Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma.
Article in World journal of surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- Prediction of Blood Transfusion Need and Dose in Patients With Upper Gastrointestinal Bleeding: Retrospective Multicenter Prediction Model Study.JMIR medical informatics · 2026Article
- Severe trauma care: advances and future directions in diagnostic and therapeutic techniques and information technology support.Medical review (2021) · 2026Article
- Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia.Current opinion in anaesthesiology · 2026Review
- Title: Multimodal Fusion of Clinical and Imaging Data for Early Prediction of Massive Transfusion in Trauma.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality.BJS open · 2025Article
- Utility of point of care viscoelastic haemostatic assays for trauma patients in the emergency department.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Observational
- MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.PloS one · 2025Article
- Femoral blood gas analysis, a new promising tool to assess hemorrhagic shock status.Annals of medicine and surgery (2012) · 2024Article
- Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024Article
- Letter to the Editor: Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma.World journal of surgery · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurately predicting which patients are most likely to benefit from massive transfusion protocol (MTP) activation may help patients while saving blood products and limiting cost. The purpose of this study is to explore the use of modern machine learning (ML) methods to develop and validate a model that can accurately predict the need for massive blood transfusion (MBT).
methodsThe institutional trauma registry was used to identify all trauma team activation cases between June 2015 and August 2019. We used an ML framework to explore multiple ML methods including logistic regression with forward and backward selection, logistic regression with lasso and ridge regularization, support vector machines (SVM), decision tree, random forest, naive Bayes, XGBoost, AdaBoost, and neural networks. Each model was then assessed using sensitivity, specificity, positive predictive value, and negative predictive value. Model performance was compared to that of existing scores including the Assessment of Blood Consumption (ABC) and the Revised Assessment of Bleeding and Transfusion (RABT).
resultsA total of 2438 patients were included in the study, with 4.9% receiving MBT. All models besides decision tree and SVM attained an area under the curve (AUC) of above 0.75 (range: 0.75-0.83). Most of the ML models have higher sensitivity (0.55-0.83) than the ABC and RABT score (0.36 and 0.55, respectively) while maintaining comparable specificity (0.75-0.81; ABC 0.80 and RABT 0.83).
conclusionsOur ML models performed better than existing scores. Implementing an ML model in mobile computing devices or electronic health record has the potential to improve the usability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.