Article

External Validation of Machine Learning Models for Traumatic Brain Injury: Performance, Efficiency, and Carbon Imprint

2026-07-08

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Background</h4> Machine learning (ML) models for traumatic brain injury (TBI) prediction increasingly demand extensive data, computational resources, and energy consumption, yet simpler models may offer comparable clinical benefit with lower barriers to deployment. This study compares predictive performance, computational efficiency, carbon footprint, and real-world feasibility of resource-...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0e7baa7a-e58d-5913-88ca-0873bd290eb5
DOI: 10.64898/2026.07.05.26357337

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

External Validation of Machine Learning Models for Traumatic Brain Injury: Performance, Efficiency, and Carbon ImprintDOI 10.64898/2026.07.05.26357337Read

Multicohort development and validation of a machine learning model to predict six-month functional traumatic brain injury outcomes in a large national registrySemantic proximity Read

Enhancing the golden hour: classification of traumatic brain injury, severity, and concomitant clinical phenotypes using prehospital continuous physiological data during air transport.Semantic proximity Read

Survival Prediction in Traumatic Brain Injury Patients Using Machine Learning AlgorithmsSemantic proximity Read

Predicting Traumatic Brain Injury Outcomes with Machine Learning: Development and External Validation in Two Indonesian Tertiary HospitalsSemantic proximity Read

A Computational Model to Predict Brain Trauma Outcome in the Intensive Care UnitSemantic proximity Read

Performance of the MOST Score for Severe Traumatic Brain Injury: First External Validation in Resource-Limited Neurosurgical SystemsSemantic proximity Read

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning.Semantic proximity Read

Data-driven characterization of traumatic brain injury severity from clinical, neuroimaging, and blood-based indicatorsSemantic proximity Read

Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic MorbiditySemantic proximity Read

Comparative Performance of Clinical Scoring Systems for Early Mortality Prediction in Blunt Traumatic Brain InjurySemantic proximity Read

Machine-Learning Enhanced Prediction of Need for Hemorrhage Resuscitation after Trauma – The ShockMatrix Pilot StudySemantic proximity Read

Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenSemantic proximity Read