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

使用纵向 EHR、索赔和死亡率数据预测自残、自杀未遂和自杀死亡

基本信息

批准号：
10116483
负责人：
Jyotishman Pathak
金额：
$ 68.62万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-24 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10116483
关键词：
Accident and Emergency department Address Admission activity Adult Age Age-Years Big Data Bipolar Disorder Caring Cause of Death Cessation of life Characteristics Clinical Clinical Data Clinical Decision Support Systems Code Data Death Certificates Discharge Plannings Electronic Health Record Emergency Department patient Emergency department visit Emotional Ethnic Origin Feeling suicidal Female Funding Gender Gold Health Health Personnel Health system Hospitalization Hospitals Individual Inpatients Intervention Judgment Lead Life Link Machine Learning Medicine Mental Depression Mental Health Mental disorders Methods Modeling Monitor National Institute of Mental Health Natural Language Processing New York City Patient-Focused Outcomes Patients Pattern Pharmaceutical Preparations Pilot Projects Population Presbyterian Church Race Recording of previous events Research Research Methodology Research Project Grants Risk Risk Assessment Risk Factors Sampling Schizophrenia Self-Injurious Behavior Services Site Substance Use Disorder Suicide Suicide attempt Techniques Text Time Uncertainty Washington base clinical decision support cohort data integration data registry demographics detection method health care service utilization high risk implementation study improved indexing insurance claims machine learning algorithm machine learning method male model design mortality novel point of care portability predictive modeling psychiatric emergency risk prediction risk prediction model social stressor suicidal suicidal behavior suicidal patient suicidal risk suicide rate validation studies

项目摘要

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.

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。