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Delirious mania is an understudied psychiatric disorder with a mortality rate as high as 75%. Previous case studies suggest that electroconvulsive therapy (ECT) may be an effective treatment for delirious mania, though this procedure may not always be a viable option. We describe the case of a 20-year old patient, with no previous psychiatric history, who developed delirious mania over the course of four months. ECT was not a viable option for this patient due to his religious beliefs, so alternative treatment modalities were explored. After failing trials of risperidone and olanzapine, significant improvements in symptoms were exhibited with a trial of clozapine. We propose that clozapine may be an effective option in cases of delirious mania, when ECT is not a viable option. Additional research is still necessary to understand the pathology of this condition and potential treatment modalities.

This paper presents an approach for performing knowledge discovery in texts through qualitative and quantitative analyses of high‐level textual characteristics. Instead of applying mining techniques on attribute values, terms or keywords extracted from texts, the discovery process works over conceptss identified in texts. Concepts represent real world events and objects, and they help the user to understand ideas, trends, thoughts, opinions and intentions present in texts. The approach combines a quasi‐automatic categorisation task (for qualitative analysis) with a mining process (for quantitative analysis). The goal is to find new and useful knowledge inside a textual collection through the use of mining techniques applied over concepts (representing text content). In this paper, an application of the approach to medical records of a psychiatric hospital is presented. The approach helps physicians to extract knowledge about patients and diseases. This knowledge may be used for epidemiological studies, for training professionals and it may be also used to support physicians to diagnose and evaluate diseases.