ArticleScientific reports2024
Refining outcome prediction after traumatic brain injury with machine learning algorithms.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Explainable machine learning of PROGRESS-Plus social factors predicts cognitive trajectories after traumatic brain injury.Scientific reports · 2026Article
- An interpretable multimodal model for early prediction of delayed hematoma progression in frontal lobe contusion: a machine learning approach.Frontiers in neuroscience · 2026Article
- Traumatic brain injury recovery prediction by harmonizing real brain CT and synthetic brain MRI: a pilot study.Brain communications · 2026Article
- E-TBI: explainable outcome prediction after traumatic brain injury using machine learning.Medical & biological engineering & computing · 2025Article
- Incidence and risk factors of extremely poor functional outcome after burr-hole evacuation of chronic subdural hematoma in octogenarians and nonagenarians- a retrospective cohort analysis.BMC anesthesiology · 2025Article
- Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.NPJ digital medicine · 2025Article
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Unravelling Secondary Brain Injury: Insights from a Human-Sized Porcine Model of Acute Subdural Haematoma.Cells · 2024Article
- Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.Frontiers in public health · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Outcome after traumatic brain injury (TBI) is typically assessed using the Glasgow outcome scale extended (GOSE) with levels from 1 (death) to 8 (upper good recovery). Outcome prediction has classically been dichotomized into either dead/alive or favorable/unfavorable outcome. Binary outcome prediction models limit the possibility of detecting subtle yet significant improvements. We set out to explore different machine learning methods with the purpose of mapping their predictions to the full 8 grade scale GOSE following TBI. The models were set up using the variables: age, GCS-motor score, pupillary reaction, and Marshall CT score. For model setup and internal validation, a total of 866 patients could be included. For external validation, a cohort of 369 patients were included from Leuven, Belgium, and a cohort of 573 patients from the US multi-center ProTECT III study. Our findings indicate that proportional odds logistic regression (POLR), random forest regression, and a neural network model achieved accuracy values of 0.3-0.35 when applied to internal data, compared to the random baseline which is 0.125 for eight categories. The models demonstrated satisfactory performance during external validation in the data from Leuven, however, their performance were not satisfactory when applied to the ProTECT III dataset.
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