In an article published today in the online journal npj Digital Medicine, Dr. Zachary Kaminsky, DIFD-Mach-Gaensslen Chair in Suicide Prevention Research at The Royal, explains an algorithm he has developed that can identify suicidal ideation based on an individual’s Twitter posts. The algorithm is called the Suicide Artificial Intelligence Prediction Heuristic or SAIPH.
SAIPH not only assesses an individual’s future risk of suicidal thought, but also when they will be at risk, based on publicly available data in Twitter posts. Algorithmic approaches like SAIPH have the potential to identify individual future suicide risk and could be adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.
To learn more, check out the article in npj Digital Medicine (published by Nature): A machine learning approach predicts future risk to suicidal ideation from social media data https://www.nature.com/articles/s41746-020-0287-6#Sec2