Predictive modeling in e-mental health: A common language framework

Abstract

Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

A Framework for Applying Natural Language Processing in Digital Health Interventions

“…In step 2 (Figure 2), supervised learning approaches [39] are utilized to (A) infer symptom severity over time; (B) predict a therapeutic outcome, which could include premature dropout; and (C) infer message characteristics. These models are explained below:…”

‘AI gone mental’: engagement and ethics in data-driven technology for mental health

“…In their paper on predictive modeling in e-mental health, Becker et al (2018) reveal an example of some flawed thinking around PPI. Predictive modeling is a type of machine learning in AI that uses big and personal data to find patterns to predict future events.…”

“…Their proposals are broader than PPI, but nonetheless form an important argument for involvement, based in the need to build trust and accountability in machine learning systems, particularly because AI is "being increasingly used to make predictions about the likelihood of future events occurring" (RSA, 2018, p. 6). This includes prediction of mental health problem onset or relapse by clinicians using personal data (Becker et al, 2018), which has significant ethical implications for data ownership and fully informed consent for data use (including information on risk), algorithmic accountability and the right to explanation. The RSA propose a working definition of ethical AI that addresses these and other issues.…”

Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care

“…More recently, in clinical psychology and psychotherapy, algorithms are used that predict from which treatment a patient benefits the most [34]. As an example, Becker and colleagues [35] introduce a conceptual framework that helps classifying applications of predictive modeling in mental health research. These authors try to bridge the gap between psychologists and predictive modelers with providing a common language for classifying predictive modeling mental health research.…”