Recent interest in the external validity of prediction models has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments. However, these methods consider different types of shifts and have been developed under disparate frameworks, making it difficult to theoretically analyze how solutions differ with respect to stability and accuracy. Taking a causal graphical view, we use a flexible graphical representation to express various types of dataset shifts. (...) Given a known graph of the data generating process, we show that all invariant distributions correspond to a causal hierarchy of graphical operators, which disable the edges in the graph that are responsible for the shifts. The hierarchy provides a common theoretical underpinning for understanding when and how stability to shifts can be achieved, and in what ways stable distributions can differ. We use it to establish conditions for minimax optimal performance across environments, and derive new algorithms that find optimal stable distributions. By using this new perspective, we empirically demonstrate that that there is a tradeoff between minimax and average performance. (shrink)

The aim of this chapter is to describe a type of law governing involuntary treatment that is based on decision-making capability and not on risk of harm to self or others. It is consistent with the legal and ethical principles followed in general medicine, and non-discriminatory against people with a mental illness. The rationale behind the proposal is outlined, as well as its principles and main features. It is argued that this type of law could be adapted to the needs (...) of low and middle-income countries without sacrificing its underlying principles, and that it lends itself to such adaptation much better than traditional mental health legislation. (shrink)

The documented efficacy and long-term benefit of antidepressants in patients with recurrent forms of severe anxiety or depressive disorders support their use in those individuals with these disorders, who experience suicidal thoughts or behavior. In general, it is assumed that antidepressants are beneficial for all symptoms of depression, including suicidality. However, some evidence suggests that Selective Serotonin Reuptake Inhibitors [SSRIs] may cause worsening of suicidal ideas in vulnerable patients. Systematic reviews and pooled analysis of experimental, observational, and epidemiological studies have (...) investigated the use of SSRIs and their association with suicidality. Taking account of the methodological limitations of these studies, the current evidence fails to provide a clear relationship between their use and risk of suicidality in adults. However, in children and adolescents, there appears to be a bit of increased risk of suicidal ideations and attempts, but not of completed suicides. This risk can be anticipated and managed clinically. Clinicians are, therefore, advised to maintain a close follow-up during the initial treatment periods and remain vigilant of this risk. This advisory, however, should not deter clinicians from the use of effective dosages of antidepressants for a sufficient period of time, in every age group of patients, when clinically needed, and if found suitable otherwise. (shrink)

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. In the (...) second scenario, a comparable AI model hybridized with genetic algorithm as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient and lowest value root mean square error. However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model. (shrink)