Discovery of generalizable TBI phenotypes using multivariate time-series clustering.
Generalizability across datasets
Multivariate time-series clustering
Phenotyping
SLAC-Time
Self-supervised learning
Transformer
Traumatic brain injury
Journal
Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250
Informations de publication
Date de publication:
12 Aug 2024
12 Aug 2024
Historique:
received:
15
01
2024
revised:
15
07
2024
accepted:
02
08
2024
medline:
14
8
2024
pubmed:
14
8
2024
entrez:
13
8
2024
Statut:
aheadofprint
Résumé
Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, β, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype β signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.
Identifiants
pubmed: 39137674
pii: S0010-4825(24)01082-5
doi: 10.1016/j.compbiomed.2024.108997
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
108997Informations de copyright
Copyright © 2024 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The author Prof. Dr. Andrea Wichelhaus codeveloped the RED bracket. The RED bracket is manufactured by Redsystem and she is a shareholder of said company. All other authors declare that they have no potential conflicts of interest.