Mild traumatic brain injury (mTBI) is a growing public healthcare concern, and presents a substantial burden to patients, families and health care systems (Langer et al., 2020). The incidence rate of this silent epidemic has significantly increased over the past decade, and accounts for the majority of all reported traumatic brain injury cases (Rao et al., 2017). While the majority of patients recover within 3 months, up to 30% of patients experience persistent concussion symptoms, affecting their ability to return to school, work, and activities of daily living (Dennis et al., 2019; Permenter et al., 2021). Given the significant increase in concussion and economic burden to health care systems, there is a need for effective and efficient evaluation of these injuries by healthcare professionals to implement accurate and timely management strategies.

Concussion reflects in a variety of affected systems and areas of co-occurring disruption that requires interdisciplinary management, an approach that is universally recommended in consensus statements and best-practice clinical guidelines (Collins et al., 2016; McCrory et al., 2017; Schneider et al., 2019). It is the heterogeneous nature of concussion, from its etiology and pathophysiology, to its individual clinical presentation and variable recovery trajectories, that has made its management especially challenging for the clinician or primary care physician. Moreover, in regions or health care systems where interdisciplinary care falls outside insured or core medical services, translating concussion best-practices in the application of the interdisciplinary treatment model represents a further barrier toward effective concussion management and meaningful progress in addressing the concussion epidemic.

Growing evidence of the complexity of concussion has given rise to the development of clinical subtypes, with steady empirical support (Collins et al., 2014; Ellis et al., 2016, 2018; Kontos and Collins, 2018; Lumba-Brown et al., 2020), and is considered a valuable tool toward informing clinical decision making, treatment planning and the conceptualization of targeted rehabilitation pathways. Similar to Collins et al. (2014) early delineation of six distinct sport-related concussion subtypes based on patient-reported symptoms following one-week post-injury, subsequent subtypes, or post-concussion disorders, have been organized according to symptoms, impairments or a combination of affected systems.

However, these subtypes may be vulnerable to bias in their conceptualization (Langdon et al., 2020), or inherently limited by the application of discipline specific expertise. Certainly, there is a disproportionate body of research on sport related concussion with notable gaps in our understanding of mTBI associated with motor vehicle accidents and other causes. Moreover, the complexity of concussion and its multi-factorial nature does not align with the approaches that assimilate the dimensionality of concussion into its basic units of impairment. A reductionism approach–that isolates a single factor, or combination of factors, and assumes them to be the cause of injury or impairment (Hulme and Finch, 2015)–may be useful in understanding causal, linear relationships (Bittencourt et al., 2016), however the non-linear, multifaceted entity that is concussion may require more sophisticated methods to capture the complex determinants that influence outcomes. As Langer et al. (2020) suggested, such a model requires “testing in an overview of empirical evidence focusing on data driven clustering of symptoms” into concussion subtypes. Kontos and Collins (2018), identified the need for mapping symptom clusters across various domains that quantified symptom clusters of concussion into objective deficits in functional outcome domain measures.

Analyses that integrate Artificial Intelligence (AI) and embrace a systems approach to concussion, with inherent non-linearity and complex, dynamic interactions, may improve our ability to identify patterns of system disruption in such multidimensional injuries as concussion. Recent AI work has used machine learning to predict symptom resolution following sport-related concussion (Bergeron et al., 2019). While another study used a clustering approach on vestibular and balance diagnostic data, and demonstrated two clinically distinct groups, patients with prominent vestibular disorders and others with no clear vestibular or balance impairment (Visscher et al., 2019). Specifically in consideration of the heterogeneity of concussion and noting the ways in which this complicates research efforts, Kenzie et al. (2018) utilized causal loop diagramming to visualize relationships between concussion injury factors, including pathophysiology, deficits, symptom persistence and recovery trajectories.

Advances in machine learning can provide a distinct practical advantage to healthcare providers (Davenport and Kalakota, 2019). The advent of using machine learning in addressing complex healthcare questions is already underway, demonstrating promise in automating and assisting in clinical diagnoses and treatment response (Garcia-Vidal et al., 2019; Nakata, 2019; Stevens et al., 2019). Compared to traditional statistics, machine learning can identify non-linear relationships and high-order interactions between multiple variables, where traditional statistics fall short (Bergeron et al., 2019). Thus, machine learning could more appropriately address multifaceted and complicated human health conditions, such as concussion.

Hierarchical agglomerative clustering, is a unsupervised, bottom-up, machine learning approach that can identify subgroups from complex data and provide an opportunity to classify clinical patterns as well as create novel representations of clinical profiles (Hassan et al., 2020). There are substantial implications for research on concussion subtypes, but also the utilization of machine learning to help in interpreting overall assessment results and summarizing multiple parameters to identify which features or combination of features discriminate between clinical profiles. This approach aligns with the heterogeneity, complexity, and diversity of concussion.

The purpose of this study was to determine whether a bottom-up, unsupervised, machine learning approach, could provide insight into different concussion clinical profiles by using 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). Utilization of self-administered, objective outcome measures, without the costly barriers of interdisciplinary concussion assessment were prioritized for this study.

Concussion is only seeming to grow in complexity as research and evidence advance our understanding of its heterogenous nature. From its etiology and pathophysiology, to its individual clinical presentation and variable recovery trajectories, clinical management has been especially challenging for the clinician or primary care physician. Research has varied widely in how it has dealt with this increasing complexity, with some seeming to lean into the complexity by highlighting systems approaches and recursive modeling (Schneider et al., 2019), while others argue that its heterogeneity is associated with mutually reinforcing biopsychosocial symptoms rather than a single entity (Iverson, 2019).

Clinically, management of the concussion patient has continued to grow in complexity as well, with increasing challenge for the primary care doctor or individual clinician, particularly in context of westernized medical frameworks and reimbursement systems. Multiple risk factors have been identified for prolonged recovery from concussion such as previous concussion history, developmental delay, headache history and psychiatric history, all of which require consideration together with a multitude of other variables that can influence outcomes. Clinical assessment of the concussion patient requires consideration of the above as well as potential interactions between them, all of which form the unique patterns of presentation in the individual concussion patient. AI is an ideal, and perhaps even necessary, partner, that can best support our ability to manage the complexity of concussion, toward gaining a better understanding of patterns that may improve our ability to diagnose and treat mTBI.

This study evaluated the use of an unsupervised, bottom-up, machine learning approach to analyze the clinical profiles of patients attending a private concussion clinic. Utilization of objective outcome measures provided a unique opportunity to engage machine learning with various concussion and non-concussion specific evidence-based metrics. Findings revealed five statistically significant and distinct clusters, each with unique patterns of system disruption.

Across each of these five clusters, 14 features were retained, which combined outcomes from PROMIS, DHI, and ImPACT. These 14 features were: Pain Interference, Pain Intensity, Physical Function and Mobility, Anxiety, Depression, Sleep Disturbance, Ability to Participate in Social Roles, Cognitive Function, Fatigue, DHI Total, DHI Functional, Visual Motor Speed, Reaction Time, and Verbal Memory. Reliable and validated self-administered outcome measures were specifically utilized to ensure the accessibility and affordability of this approach.

The authors considered each of five clusters, or concussion subtypes to be best understood in terms of Complexity, with the following classification system suggested for each concussion subtype: Minimally Complex (Cluster 2), Mildly Complex (Cluster 3), Moderately Complex (Cluster 1), Highly Complex (Cluster 0), Extremely Complex (Cluster 4) (Figure 4). Level of complexity was ranked according to number and relative severity of arenas affected. “Complexity” was considered to better align with the nature and particular features of concussive injury, informing specific and treatable features that support clinical decision making. The language of “complexity” was furthermore preferred to “severity” to promote perceived control, in both practitioner and patient, while avoiding the catastrophizing that might arise with a severity approach.

Historically, concussion classification approaches have grouped subtypes according to particular symptoms or a combination of affected systems. From the concussion profiles suggested by Collins et al. (2014) to the most recent subtypes as proposed by Lumba-Brown et al. (2020) and Kontos et al. (2020), the approach to subtyping has been grounded in heuristics derived from clinical experience and patient presentation or reporting.

The utilization of AI offered an opportunity to engage machine learning pattern recognition to explore what clinical concussion data can inform regarding concussion profiles. Indeed, this analysis revealed five unique and distinct subtypes with various patterns of system disruption, across multiple symptoms areas. This machine learning approach may be most appropriate for capturing the complexity and heterogeneity of the dynamic injury that is concussion, and in doing so, maximizing the potential of these subtypes to support and improve clinical decision making in concussions.

Reliable and validated self-administered outcome measures were specifically utilized to ensure the accessibility and affordability of this approach, offering a data-driven method toward enhancing clinical judgement and decision making without requiring commonly utilized clinicaly administered measures. The utilization of self-administered objective measures is of notable value in countries and regions that are more dispersed with less access to the recommended interdisciplinary concussion team otherwise needed to assess or screen the range of clinical areas affected in concussion.

Moreover, the Coronavirus pandemic emphasized the need for virtual tools to support and inform concussion care when in-person options are not available. The self-administered nature of the tools utilized in this analysis, as well the interdisciplinary value of the information provided, may well reduce costs or make affordable a reliable screening for those in a fee-for-service environment, as well as for the health systems that don't include interdisciplinary care among their insured services.

The datasets presented in this article are not readily available because these data are clinical information obtained from a private healthcare database. Requests to access the datasets should be directed to crosenblatt@advanceconcussion.com.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

CKR conceived the investigation with contribution by SAR. CKR and ANB designed the study. CKR, ANB, and SAR prepared the data. ANB trained and tested the algorithm with contribution by CKR. CKR and ANB interpreted the results. AH contributed to interpretation. AH and CKR drafted the paper and then substantially revised the paper with contributions by ANB. All authors contributed to the article and approved the submitted version.

The authors declare the potential following conflicts of interest with respect to the research, authorship, and/or publication of this article: AH, ANB, and SAR are employed by Advance Concussion Clinic. CKR is the Founder and Clinical Director of Advance Concussion Clinic.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.