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IntroductionBetween 25–50% of psychiatric patients are non-compliant with their pharmacological treatment. When differences between compliant and non-compliant patients were analyzed, differences were found in relation to their beliefs and feelings about medication. The Drug Attitude Inventory (DAI) was created to measure attitudes towards medication in adults. It predicted adherence in schizophrenia and depression studies.ObjectiveDetermine if psychotherapeutic and psychoeducational activities – during a partial hospitalization at the Psychiatric Day Hospital – can improve aspects related to feelings and thoughts about medication.MethodWe gathered retrospectively a sample of 151 patients hospitalized at the Psychiatric Day Hospital, from September 2013 to June 2015. Their thoughts and feelings about medication were measured with the DAI before and after the hospitalization. From the sample of 151 patients, 94 completed both tests, excluding who did not have the final DAI score. Differences between initial and final scores were statistically analyzed with the Wilcoxon test for paired samples.ResultsOf the 94 patients who completed the study, 52 showed an improvement in their DAI score, whereas the remaining 27 showed an equal or decreased final DAI compared to initial evaluation. The difference was statistically significant (P ≤ 0.05).ConclusionIt seems that psychoeducational activities related to medication are important in order to reconsider or modify feelings and thoughts about treatment. Information on medication provided to psychiatric patients (to those who need psychopharmacological treatment), carried out in a group context, which facilitates an open and sincere communication, can be a useful strategy to improve compliance with treatment.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Schizophrenia is a chronic and severe mental disorder that affects individuals in various ways, particularly in their ability to perceive, process, and respond to stimuli. This condition has a significant impact on a considerable number of individuals. Consequently, the study, analysis, and characterization of this pathology are of paramount importance. Electroencephalography (EEG) is frequently utilized in the diagnostic assessment of various brain disorders due to its non-intrusiveness, excellent resolution and ease of placement. However, the manual analysis of electroencephalogram (EEG) recordings can be a complex and time-consuming task for healthcare professionals. Therefore, the automated analysis of EEG recordings can help alleviate the burden on doctors and provide valuable insights to support clinical diagnosis. Many studies are working along these lines. In this research paper, the authors propose a machine learning (ML) method based on the eXtreme Gradient Boosting (XGB) algorithm for analyzing EEG signals. The study compares the performance of the proposed XGB-based approach with four other supervised ML systems. According to the results, the proposed XGB-based method demonstrates superior performance, with an AUC value of 0.94 and an accuracy value of 0.94, surpassing the other compared methods. The implemented system exhibits high accuracy and robustness in accurately classifying schizophrenia patients based on EEG recordings. This method holds the potential to be implemented as a valuable complementary tool for clinical use in hospitals, supporting clinicians in their clinical diagnosis of schizophrenia.