ArticleBiomedicines2022
Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way?
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- The Meaning of Pain in the Clinical Management of Patients With Disorders of Consciousness: Is It a Neglected Issue?European journal of pain (London, England) · 2026Review
- Supervised Learning Algorithm Comparison in Discharge Status Prediction of Trauma Patients: Empirical Evaluation.Journal of biomedical physics & engineering · 2026Article
- Development and validation of a prognostic prediction model for patients with traumatic multiple fractures and hemorrhagic shock using an Automated Machine Learning framework: a retrospective cohort study.Frontiers in medicine · 2026Article
- Soft-voting classifier for outcome prediction in traumatic brain injury patients with clear consciousness: model development and error pattern analysis.Neurosurgical review · 2025Article
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review.World journal of critical care medicine · 2025Review
- Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.CNS neuroscience & therapeutics · 2024Article
- Traumatic Brain Injury Rehabilitation Outcome Prediction Using Machine Learning Methods.Archives of rehabilitation research and clinical translation · 2023Article
- Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning and network analysis for diagnosis and prediction in disorders of consciousness.BMC medical informatics and decision making · 2023Article
- Prognosis prediction in traumatic brain injury patients using machine learning algorithms.Scientific reports · 2023Article
- Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study.Intelligence-based medicine · 2023Article
- Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics.Biomedicines · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. This study aims at providing this comparison on a sample of TBI patients evaluated at baseline (T0), after 3 months from the event (T1), and at discharge (T2). A Classical Linear Regression Model (LM) was compared with independent performances of Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes (NB) and Decision Tree (DT) algorithms, together with an ensemble ML approach. The accuracy was similar among LM and ML algorithms on the analyzed sample when two classes of outcome (Positive vs. Negative) approach was used, whereas the NB algorithm showed the worst performance. This study highlights the utility of comparing traditional regression modeling to ML, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons.
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.