Machine Learning for Subtyping Concussion Using a Clustering Approach

Cirelle K Rosenblatt; Alexandra Harriss; Aliya-Nur Babul; Samuel A Rosenblatt

doi:10.3389/fnhum.2021.716643

Machine Learning for Subtyping Concussion Using a Clustering Approach

Front Hum Neurosci. 2021 Sep 30:15:716643. doi: 10.3389/fnhum.2021.716643. eCollection 2021.

Authors

Cirelle K Rosenblatt^{1

2}, Alexandra Harriss¹, Aliya-Nur Babul³, Samuel A Rosenblatt¹

Affiliations

¹ Advance Concussion Clinic Inc., Vancouver, BC, Canada.
² Division of Sport & Exercise Medicine, Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
³ Department of Astronomy, Columbia University, New York, NY, United States.

Abstract

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 determine whether a bottom-up, unsupervised, machine learning approach, could more accurately support concussion subtyping. Methods: Initial patient intake data as well as objective outcome measures including, the Patient-Reported Outcomes Measurement Information System (PROMIS), Dizziness Handicap Inventory (DHI), Pain Catastrophizing Scale (PCS), and Immediate Post-Concussion Assessment and Cognitive Testing Tool (ImPACT) were retrospectively extracted from the Advance Concussion Clinic's database. A correlation matrix and principal component analysis (PCA) were used to reduce the dimensionality of the dataset. Sklearn's agglomerative clustering algorithm was then applied, and the optimal number of clusters within the patient database were generated. Between-group comparisons among the formed clusters were performed using a Mann-Whitney U test. Results: Two hundred seventy-five patients within the clinics database were analyzed. Five distinct clusters emerged from the data when maximizing the Silhouette score (0.36) and minimizing the Davies-Bouldin score (0.83). Concussion subtypes derived demonstrated clinically distinct profiles, with statistically significant differences (p < 0.05) between all five clusters. Conclusion: This machine learning approach enabled the identification and characterization of five distinct concussion subtypes, which were best understood according to levels of complexity, ranging from Extremely Complex to Minimally Complex. Understanding concussion in terms of Complexity with the utilization of artificial intelligence, could provide a more accurate concussion classification or subtype approach; one that better reflects the true heterogeneity and complex system disruptions associated with mild traumatic brain injury.

Keywords: artificial intelligence; cluster analysis; complexity; concussion; interdisciplinary; mild traumatic brain injury; rehabilitation.