Machine Learning for Subtyping Concussion Using a Clustering Approach

• Published in: Frontiers in human neuroscience Vol. 15; p. 716643
• Main Authors: Rosenblatt, Cirelle K., Harriss, Alexandra, Babul, Aliya-Nur, Rosenblatt, Samuel A.
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 30.09.2021; Frontiers Media S.A
• Subjects: Artificial intelligence; Cluster analysis; Clustering; Cognitive ability; Concussion; Health care; Human Neuroscience; interdisciplinary; Interdisciplinary aspects; Learning algorithms; Machine learning; mild traumatic brain injury; Pain; Pathophysiology; Patients; Principal components analysis; Recovery (Medical); rehabilitation; Software; Statistical analysis; Traumatic brain injury
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2021.716643

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.