  More than one million people commit suicide every year worldwide. The costs
of daily cares, social stigma and treatment issues are still hard barriers to
overcome in mental health. Most symptoms of mental disorders are related to the
behavioral state of a patient, such as the mobility or social activity.
Mobile-based technologies allow the passive collection of patients data, which
supplements conventional assessments that rely on biased questionnaires and
occasional medical appointments. In this work, we present a non-invasive
machine learning (ML) model to detect behavioral shifts in psychiatric patients
from unobtrusive data collected by a smartphone app. Our clinically validated
results shed light on the idea of an early detection mobile tool for the task
of suicide attempt prevention.
