A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships

We design a metric learning approach that aims to address computational challenges that yield from modeling human outcomes from ambulatory real-life data. The proposed metric learning is based on a Siamese neural network (SNN) that learns the relative difference between pairs of samples from a target user and non-target users, thus being able to address the scarcity of labelled data from the target. The SNN further minimizes the Wasserstein distance of the learned embeddings between target and non-target users, thus mitigating the distribution mismatch between the two. Finally, given the fact that the base rate of focal behaviors is different per user, the proposed method approximates the focal base rate based on labelled samples that lay closest to the target, based on which further minimizes the Wasserstein distance. Our method is exemplified for the purpose of hourly stress classification using real-life multimodal data from 72 dating couples. Results in few-shot and one-shot learning experiments indicate that proposed formulation benefits stress classification and can help mitigate the aforementioned challenges

Regulating digital therapeutics for mental health: Opportunities, challenges, and the essential role of psychologists

With so many promising digital therapeutics for anxiety and obsessive-compulsive (OC) spectrum problems, there is an urgent need to consider how evolving regulatory oversight of digital therapeutics is poised to shift how these tools are developed, evaluated, reimbursed, and delivered. In this commentary, we discuss both opportunities and potential pitfalls associated with emerging government regulations of digital therapeutics for mental health, and we consider how applying the traditional 'prescription-based' medical approval paradigm to digital therapeutics for mental health could ultimately undermine and limit the broad accessibility of these software-based innovations that have been explicitly designed to expand the accessibility of care. For example, the vast majority of behavioural and mental health providers do not have 'prescription privileges' (a term originally rooted in pharmacologic practices), and as a result, under current regulations in the U.S. would not be authorized to make FDA-cleared digital therapeutics available to their patients. This is particularly concerning given that most digital therapeutics for mental health are directly rooted in psychological and behavioural science, yet psychologists would not be authorized to incorporate these innovations into their practice. We consider how synchronizing regulatory standards across countries may prove useful, and we conclude by arguing that multidisciplinary teams making regulatory decisions concerning digital therapeutics for mental health must include representation from the discipline and practice of psychology. PRACTITIONER POINTS: Emerging government regulations of digital therapeutics for mental health present both opportunities and potential pitfalls Applying the traditional 'prescription-based' medical approval paradigm to digital therapeutics for mental health could ultimately undermine the broad accessibility of these software-based innovations. Synchronizing regulatory standards across countries may prove useful. Multidisciplinary teams making regulatory decisions concerning digital therapeutics for mental health must include representation from the field of psychology