Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multi-class, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.

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Graves’ disease is the most common cause of hyperthyroidism. It has an autoimmune basis with the activating thyrotropin-receptor antibodies inducing thyroid hormone overproduction. The most common manifestations of hyperthyroidism are weight loss, fatigue, heat intolerance, tremor, and palpitations, but there are several other symptoms and signs associated with this condition. We report a case of a young woman who presented in the emergency room with acute onset of cough with mild hemoptysis and dyspnea at rest. She reported one month of insomnia, palpitations, and anxiety. The diagnostic investigation leads to the diagnosis of Graves’ disease in thyrotoxic crisis presenting with flash pulmonary edema. Therapy with propranolol and methimazole was instituted with remarkable clinical improvement.