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Background: Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records., Methods: In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure)., Results: In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61)., Conclusions: It is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges., (Copyright © 2020 Senior, Burghart, Yu, Kormilitzin, Liu, Vaci, Nevado-Holgado, Pandit, Zlodre and Fazel.)

Online advertising is an important tool that can be utilized by charities to elicit attention and funding. A critical examination of advertisement strategies is thus necessary to increase the efficacy of fundraising efforts. Previous studies have shown that individuals' moral views and perceptions of social norms can play important roles in charitable behavior. Thus, the current protocol describes a study to examine whether framing charitable advertisements in line with participants' morality and increasing the salience of descriptive social norms increases subsequent charitable behavior. We describe experimental, online methods, whereby participants are provided with a framed call-to-action and normative information within a custom-developed application or existing survey platform. Furthermore, in the exploratory fashion, we discuss the possibility of collecting participants' Facebook data and predicting moral profiles from this data. If there is an increased rate of donations as a result of moral compatibility and/or increased norm salience, charities can leverage this knowledge to increase the donations by tailoring their campaigns in a more appealing way for their prospective donors. Moreover, if it turns out to be possible to predict one's moral profile from Facebook footprints, charities can use this knowledge to find and target people that are more likely to support their cause. However, this introduces important ethical questions that are discussed within this protocol.