Machine Learning for Subtyping Concussion Using a Clustering Approach

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.

Identification of Concussion Subtypes Based on Intrinsic Brain Activity

“…Concussion (mTBI) is a complex and heterogeneous disorder; however, efforts to describe this heterogeneity have mainly focused on clinical symptoms . To our knowledge, this cohort study is the first to demonstrate the existence of brain activity–based subtypes within the concussion population.…”

Single-cell multimodal modeling with deep parametric inference

“…It is worth noting that to eliminate differences in clustering algorithms, the low-dimensional representations of these models are all fed into the Leiden clustering algorithm [13] with the same parameters. The results show that the DPI model outperforms SeuratV4 and TotalVI on both Calinski Harabasz score [14] and Silhouette score [15] evaluations (Fig. 3A).…”

The Future of Artificial Intelligence in Sports Medicine and Return to Play

“…Rosenblatt et al built a model based on objective data from several validated concussion scoring systems that was able to subtype concussion into five categories ranging from "minimally complex" to "extremely complex." 33…”

“…32 Proper concussion analysis and treatment protocol is crucial in athletes, and given the ever-increasing complexity, AI-based pattern recognition may provide a valuable adjunct in improving diagnosis and treatment planning. Rosenblatt et al built a model based on objective data from several validated concussion scoring systems that was able to subtype concussion into five categories ranging from “minimally complex” to “extremely complex.” 33…”