ArticlePloS one2018
Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Machine learning-based model for predicting inpatient mortality in adults with traumatic brain injury: a systematic review and meta-analysis.Frontiers in neuroscience · 2023Pooled it
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- A novel CT-based edema grading system combined with machine learning for precise prognostic prediction in traumatic brain injury.European journal of medical research · 2025Article
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.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
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- A comprehensive comparison of machine learning models for ICH prognostication: Retrospective review of 1501 intra-cerebral hemorrhage patients from the Qatar stroke database.Neurosurgical review · 2024Article
- Predicting 30-day mortality in severely injured elderly patients with trauma in Korea using machine learning algorithms: a retrospective study.Journal of trauma and injury · 2024Article
- Geriatric Trauma Outcome Score as a Mortality Predictor in Isolated Moderate to Severe Traumatic Brain Injury: A Single-Center Retrospective Study.Healthcare (Basel, Switzerland) · 2024Article
- Predicting factors for extremity fracture among border-fall patients using machine learning computing.Heliyon · 2024Article
- Machine Learning-Driven Prognostication in Traumatic Subdural Hematoma: Development of a Predictive Web Application.Neurosurgery practice · 2024Article
- Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.The neuroradiology journal · 2024Article
- Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features.Frontiers in surgery · 2024Article
- Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients.Therapeutics and clinical risk management · 2024Article
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6 authors.
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Abstract
backgroundThe purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).
methodsHospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively.
resultsUsing demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%).
conclusionsThe ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI.
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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.