Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances

Abstract

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.

Multi-domain potential biomarkers for post-traumatic stress disorder (PTSD) severity in recent trauma survivors

“…The limitations mentioned above call for the use of advanced computational and statistical methods that can co-evaluate wide arrays of potential biomarkers, disorder indicators, and clinical manifestations in PTSD. Machine learning methods are particularly well-suited to address such computational challenges, as they can account for the intricate interrelation of many relevant factors 30 . Indeed, the last decade has shown an exponential increase in the use of machine learning for the study of posttraumatic stress, including both supervised and unsupervised approaches 1,[31][32][33] .…”

Potential Neurocognitive Biomarkers for Post Traumatic Stress Disorder (PTSD) Severity in Recent Trauma Survivors

“…One way to overcome this translational gap is using advanced data-driven computational and statistical methods that can evaluate a wider arrays of potential biomarkers and disorder's indicators and quantify their relationship with clinical manifestations. Machine learning methods are particularly well-suited to confront the computational challenges, because they are able to take the complex interrelation of many relevant factors into account 41 . Indeed, in the last decade, there has been an exponential increase in machine learning approaches in the field of posttraumatic stress, including both supervised and unsupervised approaches.…”

Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure

“…Finally, machine learning models have been increasingly applied for investigating predictors for outcomes of health-related behavior (e.g. [16,20]), and particularly in the area of traumatic stress (e.g., [21,22]). In light of the evidence that cognitive and emotional information processing are important in the exacerbation of PTSD, the aim of the current study was to characterize and test the relationship and predictive accuracy of multiple relevant domains of cognitive processing and emotion recognition as they impact PTSD and distinct symptom cluster severity.…”