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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 promisingdoi:10.3389/fpsyt.2019.00036 pmid:30814958 pmcid:PMC6381841 fatcat:jkf63z33xbhh3ckgvpe6ozu7sy