ArticleScandinavian journal of trauma, resuscitation and emergency medicine2020
Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach.
Article in Scandinavian journal of trauma, resuscitation and emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models.Alpha psychiatry · 2024Pooled 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
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled it
- Future Directions and Evolution Strategies for the Clinical Management of Traumatic Brain Injury.Journal of clinical medicine · 2026Article
- Supervised Learning Algorithm Comparison in Discharge Status Prediction of Trauma Patients: Empirical Evaluation.Journal of biomedical physics & engineering · 2026Article
- An interpretable multimodal model for early prediction of delayed hematoma progression in frontal lobe contusion: a machine learning approach.Frontiers in neuroscience · 2026Article
- Glasgow Coma Scale, Revised Trauma Score, quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score to predict the 7-day in-hospital mortality in patients with traumatic brain injury: a retrospective cohort study.BMC emergency medicine · 2025Article
- A Hybrid Bidirectional Deep Learning Model Using HRV for Prediction of ICU Mortality Risk in TBI Patients.Journal of healthcare informatics research · 2025Article
- Results of therapy in children diagnosed with severe traumatic brain injury.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- "Could She/He Walk Out of the Hospital?": Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma.Diagnostics (Basel, Switzerland) · 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
- Article
- Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Settlement Is at the End-Common Trauma Scores Require a Critical Reassessment Due to the Possible Dynamics of Traumatic Brain Injuries in Patients' Clinical Course.Journal of clinical medicine · 2024Article
- Machine learning models for predicting early hemorrhage progression in traumatic brain injury.Scientific reports · 2024Article
- Article
- Refining outcome prediction after traumatic brain injury with machine learning algorithms.Scientific reports · 2024Article
- Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.The neuroradiology journal · 2024Article
- Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients.Therapeutics and clinical risk management · 2024Article
- Predicting inpatient mortality in pediatric traumatic brain injury: insights from a national database.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI).
methodsAdult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients' demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score.
resultsA total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%.
conclusionsfor prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.
Indexed as
Identifiers
What OpenQuestion holds
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.