Predicting Self-Harm, Suicide Attempt, and Suicidal Death using Longitudinal EHR, Claims and Mortality Data

项目摘要 自杀是导致死亡的主要原因之一。截至2015年，美国的年年龄调整自杀率为13.26 平均每天有121人自杀。而45到64岁的白色男性 10岁以下的人自杀的可能性是女性的4倍，女性自杀未遂的可能性是女性的3倍。 男性。最近的数据表明，自杀企图是自杀未遂的20倍，这通常被认为是一个严重的问题。 高且持续的自杀风险因素。然而，预测和监测何时有人会 试图自残和自杀几乎是不可能的。在这个项目中，我们计划利用大规模， 整合电子健康记录和来自纽约市临床数据研究网络的索赔， 与急诊（艾德）就诊或住院相关的自杀倾向。特别是，使用数据>10 我们将开发新的NLP和机器学习模型，以识别风险最高的患者， 自我伤害，自杀企图和自杀，并进行试点研究，以评估这些模型的临床效用。我们 还将使用来自Kaiser Permanente华盛顿的类似数据进行验证研究。

PROJECT ABSTRACT 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.