Substance Use and Addictive Behavior in Spanish Adolescents in Secondary School.

The detection and prevention of addictive behaviour at an early age is essential given the relationship between the age of the onset of consumption and the appearance of addiction disorders. The aim of this study was to describe the behavior related to substance use and addictive behaviors in adolescents at secondary school from 12 to 16 years of age. A cross-sectional descriptive study has been conducted. The prevalence of consumption of different addictive substances (alcohol, tobacco, cannabis, cocaine) and addictive behaviours (use of social networks and video games) were collated, and the influence of the surrounding social environment and risk perception were evaluated. The final sample was 1298 students. Alcohol, tobacco and cannabis use reflect the prevalence of last month's consumption: 14% (11.8-15.6), 15% (13.4-17.4) and 3% (1.9-2.7) respectively. 76% of the sample frequently use the Internet (5-7 days per week). There is a positive association between the frequency of use and use in the immediate environment. The relationships found show the need for educational and preventive intervention aimed at parents and students that will allow them to know and effectively deal with possible problems associated with the consumption of addictive substances.

Real-World Data and Machine Learning to Predict Cardiac Amyloidosis.

(1) Background: Cardiac amyloidosis or "stiff heart syndrome" is a rare condition that occurs when amyloid deposits occupy the heart muscle. Many patients suffer from it and fail to receive a timely diagnosis mainly because the disease is a rare form of restrictive cardiomyopathy that is difficult to diagnose, often associated with a poor prognosis. This research analyses the characteristics of this pathology and proposes a statistical learning algorithm that helps to detect the disease. (2) Methods: The hospitalization clinical (medical and nursing ones) records used for this study are the basis of the learning and training techniques of the algorithm. The approach consisted of using the information generated by the patients in each admission and discharge episode and treating it as data vectors to facilitate their aggregation. The large volume of clinical histories implied a high dimensionality of the data, and the lack of diagnosis led to a severe class imbalance caused by the low prevalence of the disease. (3) Results: Although there are few patients with amyloidosis in this study, the proposed approach demonstrates that it is possible to learn from clinical records despite the lack of data. In the validation phase, the algorithm first acted on data from the general study population. It then was applied to a sample of patients diagnosed with heart failure. The results revealed that the algorithm detects disease when data vectors profile each disease episode. (4) Conclusions: The prediction levels showed that this technique could be useful in screening processes on a specific population to detect the disease.