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Background: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. Objective: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers. Methods: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. Results: Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. Conclusion: Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.

Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. During the past years several techniques to detect unsolicited emails have been developed. This work provides means to validate the hypothesis that the identification of the email messages’ intention can be approached by sentiment analysis and personality recognition techniques. These techniques will provide new features that improve current spam classification techniques. We combine personality recognition and sentiment analysis techniques to analyse email content. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best email spam classifiers and filters to each dataset in order to compare results.

Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.

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