Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments

Abstract

Modern research has proven that the “typical patient” requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.

Predictors of mental health outcomes of three refugee groups in an advocacy-based intervention: A precision medicine perspective.

“…An additional problem with precision medicine in mental health is the molecular, neurobiological focus of much of this research (Deif & Salama, 2021). Biomarkers of mental health vulnerabilities have been unsuccessful at identifying findings that might guide clinicians in any functional way (DeRubeis, 2019) and environmental predictors have likely been overlooked (Peterson et al, 2020).…”

Digital health technologies and major depressive disorder

“…54 It is difficult to integrate patient information in a clinically relevant manner due to technical complexities in processing and analyzing data generated from multidimensional data sets and/or using artificial intelligence (AI) modeling. 54 In addition, there are various ethical concerns, including privacy and security issues and risks of social discrimination. 54 The APA has published an app evaluation model that assesses access and background, privacy and safety, clinical foundation, usability, and therapeutic goal of a given app to attempt to address these concerns based on individual patients' needs.…”

“…54 In addition, there are various ethical concerns, including privacy and security issues and risks of social discrimination. 54 The APA has published an app evaluation model that assesses access and background, privacy and safety, clinical foundation, usability, and therapeutic goal of a given app to attempt to address these concerns based on individual patients' needs. 55…”

Multimodal phenotyping of psychiatric disorders from social interaction: Protocol of a clinical multicenter prospective study

“…With the current rise of the use of Artificial Intelligence (AI) in healthcare, personalized management of mental disorders is moving forward. Hence, technologybased behavioral sensing may prove to be effective in measuring subjective communicative functioning, making inferences about symptoms, and guiding treatment management [8].…”

“…For facial landmark detection, head pose-, and facial action unit estimation we will employ OpenFace 2.0 [14]. Furthermore, we will extract body posture using the OpenPose framework [8]. To achieve the maximal possible accuracy in gaze estimation, we will combine a recent state-of-the-art approach to gaze estimation [13] with additional calibration information recorded during the interactions.…”