Subject(s)
Big Data/supply & distribution , Data Mining/methods , Datasets as Topic/supply & distribution , Information Dissemination/legislation & jurisprudence , Information Dissemination/methods , Open Access Publishing/legislation & jurisprudence , Research , Big Data/economics , Data Mining/trends , Datasets as Topic/economics , Datasets as Topic/legislation & jurisprudence , India , Open Access Publishing/economics , Research Report , Unsupervised Machine Learning/legislation & jurisprudence , Unsupervised Machine Learning/trendsABSTRACT
For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.
