Prediction of Suicide Risk Using Machine Learning and Big Data

Suicide remains a preventable, yet persistent public health challenge and is the second leading cause of death among young people. To date, the assessment of suicide risk remains largely subjective, highlighting the urgent need for more objective and precise tools to predict suicide risk at an individual level. Considering this, big data and machine learning models provide an opportunity to facilitate the early implementation of preventive and treatment strategies personalized to each patient. Therefore, in the present chapter we discuss the potential of these tools and describe recent studies using machine learning models to evaluate individualized suicide risk. Furthermore, key considerations, challenges, and the potential ethical implications of the clinical implementation of these algorithms are discussed.