Machine Learning for Subtyping Concussion Using a Clustering Approach

Machine Learning for Subtyping Concussion Using a Clustering Approach

Background: Concussion subtypes are typically organized into commonly affected symptom areas or a combination of affected systems, an approach that may be flawed by bias in conceptualization or the inherent limitations of interdisciplinary expertise. Objective: The purpose of this study was to deter...

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Bibliographic Details
Main Authors: Babul, A.-N (Author), Harriss, A. (Author), Rosenblatt, C.K (Author), Rosenblatt, S.A (Author)
Format: Article
Language:English
Published: Frontiers Media S.A. 2021
Subjects:
adult
Article
artificial intelligence
cluster analysis
cognition assessment
complexity
concussion
controlled study
Davies Bouldin score
dizziness
Dizziness Handicap Inventory
female
human
Immediate Post-Concussion Assessment and Cognitive Testing Tool
injury scale
interdisciplinary
machine learning
major clinical study
male
mild traumatic brain injury
Pain Catastrophizing scale
patient-reported outcome
Patient-Reported Outcome Measurement Information System
principal component analysis
rank sum test
rehabilitation
retrospective study
scoring system
Silhouette score
statistical significance
traumatic brain injury
unsupervised machine learning
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