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© 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 All patients admitted to an acute inpatient mental health unit must have nursing observations carried out at night either hourly or every 15 minutes, to ascertain that they are safe and breathing. However, while this practice ensures patient safety, it can also disturb patients' sleep, which in turn can impact negatively on their recovery. OBJECTIVE This article describes the process of introducing artificial intelligence ('digitally assisted nursing observations') in an acute mental health inpatient ward, to enable staff to carry out the hourly and the 15 minutes observations, minimising disruption of patients' sleep while maintaining their safety. FINDINGS The preliminary data obtained indicate that the digitally assisted nursing observations agreed with the observations without sensors when both were carried out in parallel and that over an estimated 755 patient nights, the new system has not been associated with any untoward incidents. Preliminary qualitative data suggest that the new technology improves patients' and staff's experience at night. 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. 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 &amp; 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 1.04. The older a patient is, the fewer the number of previous referrals and consenting to receiving a reminder short message service are also found to increase the likelihood of attendance with ORs of 1.02, 1.10, 1.04, respectively. CONCLUSIONS Our model is expected to help IAPT services identify which patients are not likely to attend their appointments by highlighting key characteristics that affect attendance. CLINICAL IMPLICATIONS This analysis will help to identify methods IAPT services could use to increase their attendance rates. © 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 Non-adherence to medication is associated with increased risk of relapse in patients with bipolar disorder (BD). OBJECTIVES To (1) validate patient-evaluated adherence to medication measured via smartphones against validated adherence questionnaire; and (2) investigate characteristics for adherence to medication measured via smartphones. METHODS Patients with BD (n=117) evaluated adherence to medication daily for 6-9 months via smartphones. The Medication Adherence Rating Scale (MARS) and the Rogers' Empowerment questionnaires were filled out. The 17-item Hamilton Depression Rating Scale, the Young Mania Rating Scale and the Functional Assessment Short Test were clinically rated. Data were collected multiple times per patient. [https://www.selleckchem.com/ selleck chemicals llc] The present study represents exploratory pooled reanalyses of data collected as part of two randomised controlled trials. FINDINGS During the study 90.50% of the days were evaluated as 'medication taken', 6.91% as 'medication taken with changes' and 2.59% as 'medication not tblished by BMJ.A hallmark of pancreatic ductal adenocarcinoma (PDAC) is an exuberant stroma comprised of diverse cell types that enable or suppress tumor progression. Here, we explored the role of oncogenic KRAS in protumorigenic signaling interactions between cancer cells and host cells. We show that KRAS mutation (KRAS*) drives cell-autonomous expression of type I cytokine receptor complexes (IL2rγ-IL4rα and IL2rγ-IL13rα1) in cancer cells that in turn are capable of receiving cytokine growth signals (IL4 or IL13) provided by invading Th2 cells in the microenvironment. Early neoplastic lesions show close proximity of cancer cells harboring KRAS* and Th2 cells producing IL4 and IL13. Activated IL2rγ-IL4rα and IL2rγ-IL13rα1 receptors signal primarily via JAK1-STAT6. Integrated transcriptomic, chromatin occupancy, and metabolomic studies identified MYC as a direct target of activated STAT6 and that MYC drives glycolysis. Thus, paracrine signaling in the tumor microenvironment plays a key role in the KRAS*-driven metabolic reprogramming of PDAC. SIGNIFICANCE Type II cytokines, secreted by Th2 cells in the tumor microenvironment, can stimulate cancer cell-intrinsic MYC transcriptional upregulation to drive glycolysis. This KRAS*-driven heterotypic signaling circuit in the early and advanced tumor microenvironment enables cooperative protumorigenic interactions, providing candidate therapeutic targets in the KRAS* pathway for this intractable disease. ©2020 American Association for Cancer Research.In low-grade gliomas, mutant forms of IDH1/2 trigger expression of tau, a protein typically associated with neurodegenerative disease that also inhibits EGFR signaling to impede tumor progression. The new findings provide a scientific rationale for pharmacologically mimicking the function of tau with microtubule-stabilizing drugs for the treatment of brain tumors. ©2020 American Association for Cancer Research.