Article by Daniel D’Hotman1, Erwin Loh and Julian Savulescu.
Published in BMJ Leader.
Suicide accounts for 1.5% of deaths worldwide, with over 800 000 deaths from suicide annually. Over 80% of suicides occur in low-income and middle-income countries. While significant work is taking place to reduce the impact of suicide, there is more to be done. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services.
In an attempt to reduce the impact of suicide, there is increased interest in using artificial intelligence (AI), data science and other analytical techniques to improve suicide prediction and risk identification. With the proliferation of electronic medical records (EMRs) and online platforms where people share insights on their emotional state (social media), there is now a wealth of relevant health data available to researchers. When linked with other data sources, analysis of these complex sets of information (known colloquially as ‘big data’) can provide a snapshot of biological, social and psychological state of a person at one time. Machines can learn to detect patterns, which are indecipherable using traditional forms of biostatistics, by processing big data through layered mathematical models (AI algorithms). Correcting algorithm mistakes (training) can improve the accuracy of an AI predictive model. As such, AI is well positioned to address the challenge of navigating big data for suicide prevention. [ . . . ]
Open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported license.