ArticleJournal of clinical medicine2019
Machine Learning Models of Survival Prediction in Trauma Patients.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Severe trauma care: advances and future directions in diagnostic and therapeutic techniques and information technology support.Medical review (2021) · 2026Article
- Machine learning models for early mortality prediction in trauma patients using public data: a nationwide retrospective study.World journal of emergency surgery : WJES · 2026Article
- Ensemble learning for predicting birth trauma using high-dimensional data in the neonatal intensive care unit.Scientific reports · 2026Article
- Machine learning for predicting acute lung injury after trauma: a systematic review and meta-analysis.Frontiers in medicine · 2026Article
- Atrial fibrillation risk model based on LASSO and SVM algorithms and immune infiltration of key mitochondrial energy metabolism genes.Scientific reports · 2025Article
- A machine learning model based on emergency clinical data predicting 3-day in-hospital mortality for stroke and trauma patients.Frontiers in neurology · 2025Article
- Development and validation of risk prediction model for adverse outcomes in trauma patients.Annals of medicine · 2024Article
- Development of a Machine Learning-Based Model for Predicting the Incidence of Peripheral Intravenous Catheter-Associated Phlebitis.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2024Article
- Predicting factors for extremity fracture among border-fall patients using machine learning computing.Heliyon · 2024Article
- Development and Application of a Novel Machine Learning Model Predicting Pancreatic Cancer-Specific Mortality.Cureus · 2024Article
- On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry.BMC medical informatics and decision making · 2023Article
- Analysis of the role of glucose metabolism-related genes in dilated cardiomyopathy based on bioinformatics.Journal of thoracic disease · 2023Article
- Machine learning-based prediction of in-ICU mortality in pneumonia patients.Scientific reports · 2023Article
- Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms.BMC medical research methodology · 2023Article
- Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care.World journal of emergency surgery : WJES · 2023Review
- Artificial intelligence and machine learning for hemorrhagic trauma care.Military Medical Research · 2023Review
- Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering.Diseases (Basel, Switzerland) · 2023Article
- A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database.Journal of multidisciplinary healthcare · 2023Article
- The Addition of the Geriatric Nutritional Risk Index to the Prognostic Scoring Systems Did Not Improve Mortality Prediction in Trauma Patients in the Intensive Care Unit.Emergency medicine international · 2023Article
- Decision support by machine learning systems for acute management of severely injured patients: A systematic review.Frontiers in surgery · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundWe aimed to build a model using machine learning for the prediction of survival in trauma patients and compared these model predictions to those predicted by the most commonly used algorithm, the Trauma and Injury Severity Score (TRISS).
methodsEnrolled hospitalized trauma patients from 2009 to 2016 were divided into a training dataset (70% of the original data set) for generation of a plausible model under supervised classification, and a test dataset (30% of the original data set) to test the performance of the model. The training and test datasets comprised 13,208 (12,871 survival and 337 mortality) and 5603 (5473 survival and 130 mortality) patients, respectively. With the provision of additional information such as pre-existing comorbidity status or laboratory data, logistic regression (LR), support vector machine (SVM), and neural network (NN) (with the Stuttgart Neural Network Simulator (RSNNS)) were used to build models of survival prediction and compared to the predictive performance of TRISS. Predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operating characteristic curves.
resultsIn the validation dataset, NN and the TRISS presented the highest score (82.0%) for balanced accuracy, followed by SVM (75.2%) and LR (71.8%) models. In the test dataset, NN had the highest balanced accuracy (75.1%), followed by the TRISS (70.2%), SVM (70.6%), and LR (68.9%) models. All four models (LR, SVM, NN, and TRISS) exhibited a high accuracy of more than 97.5% and a sensitivity of more than 98.6%. However, NN exhibited the highest specificity (51.5%), followed by the TRISS (41.5%), SVM (40.8%), and LR (38.5%) models.
conclusionsThese four models (LR, SVM, NN, and TRISS) exhibited a similar high accuracy and sensitivity in predicting the survival of the trauma patients. In the test dataset, the NN model had the highest balanced accuracy and predictive specificity.
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