Suicide is one of the leading causes of death. As of 2015, annual age-adjusted suicide rate in the U.S. is 13.26 per 100,000 individuals, and on average, there are 121 suicides per day. While white males between 45 and 64 years of age are 4 times more likely than females to die by suicide, females attempt suicide 3 times as often as males. Recent data suggest that there are 20 times as many suicide attempts, which is generally considered a high and consistent risk factor for subsequent suicide. However, predicting and monitoring when someone will attempt self-harm and suicide has been nearly impossible. In this project, we plan to leverage large-scale, integrated electronic health record and claims from the New York City Clinical Data Research Network to study the suicidality in relation to emergency department (ED) visits or hospitalizations. In particular, using data on >10 million patients, we will develop novel NLP and machine learning models to identify patients at highest risk for self-harm, suicide attempt and suicide, and conduct a pilot study to assess the clinical utility of such models. We will also conduct a validation study using similar data from Kaiser Permanente Washington.
As of 2015, annual age-adjusted suicide rate in the U.S. is 13.26 per 100,000 individuals, and on average, there are 121 suicides per day. Successful identification of patients who are at the risk of self-harm, suicide attempt, and suicide can lead potential clinical interventions for improving patient outcomes. This proposal will apply ?big data? techniques to identify patients at risk of suicidal behavior using large-scale integrated clinical data.
