ArticleBrain injury2024
Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot.
Article in Brain injury, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Risk Factors for Poor Outcomes of Mild and Moderate Traumatic Brain Injury Patients in Tanzania: A Retrospective Cohort Study Using Latent Class Analysis.Health science reports · 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
- Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management.Biomedicines · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveThe objective is to determine whether unsupervised machine learning identifies traumatic brain injury (TBI) phenotypes with unique clinical profiles.
methodsPilot self-reported survey data of over 10,000 adults were collected from the Centers for Disease Control and Prevention (CDC)'s National Concussion Surveillance System (NCSS). Respondents who self-reported a head injury in the past 12 months (
resultsPAM grouped respondents into five TBI clusters (phenotypes A-E). Phenotype C represented more clinically severe TBIs with a higher prevalence of symptoms and association with worse outcomes. When compared to individuals in Phenotype A, a group with few TBI-related symptoms, individuals in Phenotype C were more likely to undergo medical evaluation (odds ratio [OR] = 9.8, 95% confidence interval[CI] = 5.8-16.6), have symptoms that were not currently resolved or resolved in 8+ days (OR = 10.6, 95%CI = 6.2-18.1), and more likely to report at least moderate impact on social (OR = 54.7, 95%CI = 22.4-133.4) and work (OR = 25.4, 95%CI = 11.2-57.2) functioning.
conclusionMachine learning can be used to classify patients into unique TBI phenotypes. Further research might examine the utility of such classifications in supporting clinical diagnosis and patient recovery for this complex health condition.
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