ArticleBMC medical informatics and decision making2020
Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 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
- Functional recovery of patients with moderate and severe traumatic brain injury after extended and standard early rehabilitation.Frontiers in human neuroscience · 2026Article
- Association of early ondansetron use with 30-day mortality in patients with acute respiratory distress syndrome: a retrospective cohort study.Journal of thoracic disease · 2025Article
- Incidence and predictors of mortality among traumatic brain injury patients in Ethiopia: a systematic review and meta-analysis.Journal of trauma and injury · 2025Article
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Advances in Machine Learning for Mechanically Ventilated Patients.International journal of general medicine · 2025Review
- A nomogram for predicting early mortality in patients with traumatic brain injury requiring mechanical ventilation based on clinical laboratory data.Scientific reports · 2024Article
- An externally validated prognostic model for critically ill patients with traumatic brain injury.Annals of clinical and translational neurology · 2024Article
- Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients.Therapeutics and clinical risk management · 2024Article
- Development and Validation of an XGBoost-Algorithm-Powered Survival Model for Predicting In-Hospital Mortality Based on 545,388 Isolated Severe Traumatic Brain Injury Patients from the TQIP Database.Journal of personalized medicine · 2023Article
- Red Blood Cell Omics and Machine Learning in Transfusion Medicine: Singularity Is Near.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2023Review
- AI-Based Decision Support System for Traumatic Brain Injury: A Survey.Diagnostics (Basel, Switzerland) · 2023Review
- Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery.Scientific reports · 2023Article
- Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms.BMC medical research methodology · 2023Article
- Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning and network analysis for diagnosis and prediction in disorders of consciousness.BMC medical informatics and decision making · 2023Article
- Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury.medRxiv : the preprint server for health sciences · 2023Article
- Association between ondansetron use and mortality of patients on mechanical ventilation in the intensive care unit: a retrospective cohort study.Annals of translational medicine · 2023Article
- Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis.Surgical neurology international · 2023Review
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI).
methodsA retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance.
resultsAcross the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV.
conclusionsMachine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.
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