New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

In recent years, there has been a great development of software technology in the field of psychogeriatric research, helping to improve the quality of life of the elderly and preventing cognitive deterioration associated with aging, and thus decrease the possible dependence. The main objective of the present study is to evaluate the usability of the Long Lasting Memories program in elderly people with or without cognitive impairment in a region of Spain. For the study, users were classified into three groups: subjects with no cognitive impairment, with mild cognitive impairment and mild dementia, and they were given a usability questionnaire covering different variables. Of the 157 Spanish participants in the study, 84.1 percent answered the usability questionnaire, obtaining wide acceptance in all study groups regarding the usability of the Long Lasting Memories program. Current research begins to mark a new perspective that recognizes the need to establish a preventive strategy for degenerative diseases.

Objective: The main aim of our research is to assess the use, satisfaction, and pedagogy of software for neuropsychological rehabilitation by computer, called “Gradior™,” to obtain relevant information on the impact of information and communications technology on people with severe and prolonged mental illness. Methods: To evaluate the usability and satisfaction standards, the questionnaire “Usability survey on the use of the cognitive rehabilitation and assessment program by computer” was completed by 83 patients of the Rodríguez Chamorro Hospital. Results: The results of the study show that Gradior has 81.2% acceptance and 83.7% general assessment. This indicates that those who responded to the survey consider that the Gradior program improves cognitive functions and abilities in patients with severe and prolonged mental illness and therefore their quality of life. Conclusion: This research is oriented toward professionals of the Health Area and Systems Engineers, who develop software for neuropsychological rehabilitation with neurocognitive deficit. The purpose is to make the learning process more effective among the people who use it and to improve usability for specific groups. We hope that the reading of the work contributes to the activities, techniques and materials planned are in accordance with the needs of the population affected with cognitive disorders.