Random Forests and Clustering for Identifying Persistent Post-Traumatic Headache Phenotypes

Music City Center 

Random Forests can be used for classification and clustering. In the unsupervised Random Forest used for clustering, the proximity matrix needed for clustering can be estimated. Clustering algorithms use data to form groups of similar subjects that share distinct properties. Phenotypes can be identified using a proximity matrix generated by the unsupervised Random Forests and subsequent clustering by the Partitioning around Medoids (PAM) algorithm. PAM uses the dissimilarity matrix in its class partitioning or clustering algorithm and is more robust to noise and outliers as compared to the more commonly used k-means algorithm.

Headache is the most common type of pain resulting from mild traumatic brain injury. Roughly half of those with persistent post-traumatic headache (PPTH) also report neck pain associated with greater headache severity. Identification of biologically based phenotypes could improve our mechanistic understanding and management PPTH with concomitant neck pain. The purpose of this study was to identify PPTH subgroups who share common biological impairments in cervical muscle health, pain sensitivity, and/or functional connectivity of brain networks inv

magnetic resonance imaging (MRI)

neck pain 

Section on Statistical Learning and Data Science