Whole Person Modeling: a transdisciplinary approach to mental health research

Abstract

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning

“…To identify published papers meeting our criteria of using biopsychosocial data types to model suicide-related phenotypes, we first outline our key considerations in defining biopsychosocial domains. In accordance with the tenets of Whole Person Modeling ( 30 ), we considered biological features to be separate from clinical diagnostic categories or prescribed medications. For example, features such as comorbid somatic diseases (e.g., cancer or diabetes) are not themselves considered biological risk factors, despite their biological basis.…”

“…Such study designs – falling under the umbrella of Whole Person Modeling ( 30 ) – are enabled by large-scale biobank and clinical cohort initiatives collecting data from electronic health records (EHR), multi-modal biosamples, and detailed sociodemographic surveys, neuropsychological assessments, and lifestyle questionnaires ( 31 , 32 ). The size and breadth of the resulting datasets have also permitted the application of advanced, multivariate, machine learning (ML) approaches to the modeling of suicide-related outcomes ( 33 ).…”

“…Modeling of suicide-related phenotypes using ML and a Whole Person approach may provide insights into the complex landscape of risk factors arising from interactions of genome, phenome, and exposome ( 30 ). Beyond what is possible with simpler linear, hypothesis-testing methods that dominate the extant literature, such an approach is necessary to understand the influences of biopsychosocial risk factors when considered in consort.…”

Editorial: Psychometrics in psychiatry 2022: psychological therapy and psychosomatics

“…Ecological momentary assessment uses digital tools to sample real-time patient experiences (9). Machine learning and artificial intelligence can help customize assessments and predict outcomes (10).…”