Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot

Waltzman, Dana; Daugherty, Jill; Peterson, Alexis; Lumba-Brown, Angela

doi:10.1080/02699052.2024.2352524

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Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot

9 18 2024
By Waltzman, Dana ; Daugherty, Jill ; Peterson, Alexis ; ...
Source: Brain Inj. 38(11):880-888

Public Access Version Available on: July 18, 2025, 12:00 AM

Please check back on the date listed above.

English

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Details:

Alternative Title:

Brain Inj
Personal Author:

Waltzman, Dana ; Daugherty, Jill ; Peterson, Alexis ; Lumba-Brown, Angela

Waltzman, Dana ; Daugherty, Jill ; Peterson, Alexis ; Lumba-Brown, Angela Less -
Description:

Objective:

The objective is to determine whether unsupervised machine learning identifies traumatic brain injury (TBI) phenotypes with unique clinical profiles.

Methods:

Pilot 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 (n = 1,364) were retained and queried for injury, outcome, and clinical characteristics. An unsupervised machine learning algorithm, partitioning around medoids (PAM), that employed Gower’s dissimilarity matrix, was used to conduct a cluster analysis.

Results:

PAM 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.

Conclusion:

Machine 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.
Subjects:

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Adolescent Adult Aged Brain Concussion Brain Injuries, Traumatic Centers For Disease Control And Prevention, U.S. Cluster Analysis Female Humans Machine Learning Male Middle Aged Phenotype Pilot Projects Self Report United States Unsupervised Machine Learning Young Adult
Keywords:

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Clustering Machine Learning Phenotypes TBI Traumatic Brain Injury
Source:

Brain Inj. 38(11):880-888
Pubmed ID:

38722037
Pubmed Central ID:

PMC11323138
Document Type:

Journal Article
Funding:

CC999999/ImCDC/Intramural CDC HHSUnited States/
Volume:

38
Issue:

11
Collection(s):

CDC Public Access
Main Document Checksum:

[+]

urn:sha-512:6d487ad41f8f1aeaffc497611e6d89cd359baa550d2ef025830bbc5103984e4be546c32bd1bff619ab65768b6977552197a41d94b1c4b6890144811de85b0ce6
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