In recent years, ubiquitous devices have penetrated people’s lives, and numerous studies have been conducted to find behavioral and emotional patterns affecting health and well-being. Especially in mental healthcare, till now, the tracking of the patient’s conditions relied solely on doctor appointments and self-reported surveys, which are time-consuming and might lack objectivity. During their university years, students often suffer from accumulated stress. Thus, early diagnoses and improved monitoring are becoming vital. Exploiting the StudentLife dataset, a structured approach to predict the self-reported PANAS Negative Affect (NA), consult students and reduce university drop-outs is briefly introduced.