DISCUSSION This project suggests that the digitally assisted nursing observations could maintain patients' safety while potentially improving patients' and staff's experience in an acute psychiatric ward. The limitations of this study, namely, its narrative character and the fact that patients were not randomised to the new technology, suggest taking the reported findings as qualitative and preliminary. CLINICAL IMPLICATIONS These results suggest that the care provided at night in acute inpatient psychiatric units could be substantially improved with this technology. This warrants a more thorough and stringent evaluation. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency.  https://www.selleckchem.com/products/mitomycin-c.html  We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.BACKGROUND Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information. OBJECTIVE Our aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research. METHODS We used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures. FINDINGS Results show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably. CONCLUSIONS This study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records. CLINICAL IMPLICATIONS Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.BACKGROUND The burden of mental health disorders in Europe is well above the world average and has increased from 11.5% to 13.9% of the total disease burden in 2000 and 2015. That from dementia has increased rapidly, and overtaken that from depression as the leading component. There have been no analyses of the research activity in Europe to combat this burden. METHODOLOGY We identified research papers in the Web of Science (WoS) with a complex mental health disorders filter based on title words and journal names in the years 2001-18, and downloaded their details for analysis. RESULTS European mental health disorders research represented less than 6% of the total biomedical research. We estimate that research expenditure in Europe on mental health disorders amounted to about €5.4 billion in 2018. The Scandinavian countries, with Croatia and Estonia, published the most relative to their wealth, but the outputs of France and Romania were less than half the amounts expected. DISCUSSION AND CONCLUSIONS The burden from mental health disorders is increasing rapidly in Europe, but research was only half what would have been proportional. Suicide & self-harm, and alcohol misuse, were also neglected by researchers, particularly since the latter also causes many physical burdens, such as foetal alcohol syndrome, interpersonal violence, and road traffic accidents. Other relatively neglected subjects are sexual disorders, obsessive compulsive disorder, post-traumatic stress disorder, attention-deficit hyperactivity and sleep disorders. There is an increasing volume of research on alternative (non-drug) therapies, particularly for post-traumatic stress and eating disorders, notably in Germany. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.BACKGROUND Across England, 12% of all improving access to psychological therapy (IAPT) appointments are missed, and on average around 40% of first appointments are not attended, varying significantly around the country. In order to intervene effectively, it is important to target the patients who are most likely to miss their appointments. OBJECTIVE This research aims to develop and test a model to predict whether an IAPT patient will attend their first appointment. METHODS Data from 19 adult IAPT services were analysed in this research. A multiple logistic regression was used at an individual service level to identify which patient, appointment and referral characteristics are associated with attendance. These variables were then used in a generalised linear mixed effects model (GLMM). We allow random effects in the GLMM for variables where we observe high service to service heterogeneity in the estimated effects from service specific logistic regressions. FINDINGS We find that patients who self-refer are more likely to attend their appointments with an OR of 1.