ArticleMilitary Medical Research2021
Intelligent prediction of RBC demand in trauma patients using decision tree methods.
Article in Military Medical Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Revisiting whole blood transfusion for the massively bleeding patient: The origins of resistance to change.Transfusion · 2026Article
- Early prediction of transfusion requirements in trauma patients using explainable machine learning.Transfusion · 2026Article
- Machine Learning Models for Predicting the Need for Early Packed Red Blood Cell Transfusion in Multiple Trauma Patients.Archives of academic emergency medicine · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- A quantitative analysis of the factors that influence clinical blood demand in the Suzhou area.Scientific reports · 2025Article
- Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.Hematology, transfusion and cell therapy · 2024Article
- Forecasting demand for blood products: Towards inventory management of a perishable product.Bioinformation · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis.BMC immunology · 2023Article
- Artificial intelligence and machine learning for hemorrhagic trauma care.Military Medical Research · 2023Review
- Decision tree distinguish affective disorder diagnosis from psychotic disorder diagnosis with clinical and lab factors.Heliyon · 2022Article
- Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches.Frontiers in surgery · 2022Article
- Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China.Frontiers in medicine · 2022Article
- Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China.Frontiers in medicine · 2021Article
- Development of an automated machine learning-based prediction model and interactive tool for blood transfusion requirements in patients with severe traumatic brain injury.Frontiers in neurologyArticle
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6 authors.
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Abstract
backgroundThe vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.
methodsA total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC).
resultsFor non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657-0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633-0.751) and the XGBoost (AUC 0.71, 95% CI 0.654-0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893-0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744-0.850) and the CRT (AUC 0.82, 95% CI 0.779-0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter.
conclusionsThe classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment.
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