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Information System for Monitoring and Assessing Stress among Medical Students
The severe or prolonged exposure to stress-inducing factors in occupation-al and academic settings is a growing concern. The literature describes sev-eral potentially stressful moments experienced by medical students throughout the course, affecting cognitive functioning and learning. In this paper, we introduce the EUSTRESS Solution, that aims to create an Infor-mation System to monitor and assess, continuously and in real-time, the stress levels of the individuals in order to predict chronic stress. The Infor-mation System will use a measuring instrument based on wearable devices and machine learning techniques to collect and process stress-related data from the individual without his explicit interaction. A big database has been built through physiological, psychological, and behavioral assess-ments of medical students. In this paper, we mainly focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. In order to develop a predictive model of stress, we performed different sta-tistical tests. Preliminary results showed the neural network had the better model fit. As future work, we will integrate salivary samples and self-report in order to develop a more complex and intelligent model.