The Role of Predictive Analytics in Suicide Prevention: A Framework for Data-Driven Intervention

Abstract

Suicide is one of the leading causes of death worldwide, claiming over 700,000 lives annually and leaving countless others affected. Prevention in general usually takes on traditional approaches in the forms of programs, distinctively very limited in their nature of being not predictive of the risks at all to at best to a pretty good level. With Predictive analytics aided by extraordinary improvements in the field of Data Analytics, Artificial Intelligence (AI) and its Machine Learning, prevention from traditionally delivered methods will move with a quantum leap towards the proactive type of prevention. Below is an overview of the role that predictive analytics is playing in preventing suicides by introducing a structured framework on how to execute data-driven models, further going into a deep discussion on challenges and ethical considerations this technology has to face for a brighter future ahead, thus becoming really revolutionary for mental health care.