for the Depression Screening Data (DEPRESSD) PHQ Collaboration IMPORTANCE The Patient Health Questionnaire depression module (PHQ-9) is a 9-item self-administered instrument used for detecting depression and assessing severity of depression. The Patient Health Questionnaire-2 (PHQ-2) consists of the first 2 items of the PHQ-9 (which assess the frequency of depressed mood and anhedonia) and can be used as a first step to identify patients for evaluation with the full PHQ-9.OBJECTIVE To estimate PHQ-2 accuracy alone and combined with the PHQ-9 for detecting major depression.

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Objective: The coronavirus disease (COVID-19) pandemic and related lockdown measures have raised important questions about the impact on mental health. This study evaluated several mental health and well-being indicators in a large sample from the United Kingdom (UK) during the COVID-19 lockdown where the death rate is currently among the highest in Europe. Methods: A cross-sectional online survey with a study sample that mirrors general population norms according to sex, age, education, and region was launched 4 weeks after lockdown measures were implemented in the UK. Measures included mental health-related quality of life (World Health Organization Quality-of-Life Brief Version psychological domain), well-being (World Health Organization Well-Being Index), depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder-7), perceived stress (Perceived Stress Scale-10), and insomnia (Insomnia Severity Index). Analyses of variances, Bonferroni-corrected post hoc tests, and t tests were applied to examine mental health indicators across different sociodemographic groups (age, sex, employment, income, physical activity, relationship status). Results: The sample comprised n = 1006 respondents (54% women) from all regions of the UK. Approximately 52% of respondents screened positive for a common mental disorder, and 28% screened positive for clinical insomnia. Mean scores and standard deviations were as follows: