Sumithra Velupillai, Gergö Hadlaczky, Enrique Baca-Garcia, Genevieve M. Gorrell, Nomi Werbeloff, Dong Nguyen, Rashmi Patel, Daniel Leightley, Johnny Downs, Matthew Hotopf and Rina Dutta

Psychiatry (2019) 10:36

Abstract Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate
activity for mental health services globally. In the last 50 years a large number of tools have
been designed for suicide risk assessment, and tested in a wide variety of populations,
but studies show that these tools suffer from low positive predictive values. More recently,
advances in research fields such as machine learning and natural language processing
applied on large datasets have shown promising results for health care, and may enable
an important shift in advancing precision medicine. In this conceptual review, we discuss
established risk assessment tools and examples of novel data-driven approaches that
have been used for identification of suicidal behavior and risk. We provide a perspective
on the strengths and weaknesses of these applications to mental health-related data,
and suggest research directions to enable improvement in clinical practice.