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Article

Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes.

Journal of neurotrauma - 15 Jun 2020

Folweiler Kaitlin A, Sandsmark Danielle K, Diaz-Arrastia Ramon, Cohen Akiva S, Masino Aaron J

Abstract excerpt

The heterogeneity of traumatic brain injury (TBI) remains a core challenge for the success of interventional clinical trials. Data-driven approaches for patient stratification may help to identify TBI patient phenotypes during the acute injury period as well as facilitate targeted trial patient enrollment and analysis of treatment efficacy. In this study, we implemented an unsupervised machine learning approach...

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Source

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Identifiers

PMID: 32008422
DOI: 10.1089/neu.2019.6705
PMCID: PMC7249479

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

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