Improved multifactorial prediction of suicidal behavior through integration of multiple datasets

通过整合多个数据集改进自杀行为的多因素预测

• 批准号: 9762979
• 负责人: Ben Y Reis
• 金额: $ 51.4万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-13 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762979
• 关键词: Accounting; Adoption; Calibration; Cause of Death; Cessation of life; Chronology; Clinical; Clinical Data; Complex; Data; Data Element; Data Set; Databases; Electronic Health Record; Event; Feeling suicidal; Future; Goals; Health; Health Professional; Healthcare; Healthcare Systems; Hospitalization; Individual; Information Resources; Intervention; Legal; Life; Link; Machine Learning; Maps; Markov Chains; Medical; Medical Records; Methods; Modeling; Natural Language Processing; Outcome; Patient Self-Report; Patients; Pattern; Performance; Platelet Factor 4; Records; Reporting; Research; Research Domain Criteria; Resources; Risk; Risk Behaviors; Risk Factors; Sampling; Sensitivity and Specificity; Source; Structure; Suicide; Suicide attempt; Techniques; Testing; Text; Time; United States; Work; base; clinical risk; clinically relevant; data resource; discrete time; electronic structure; health care settings; high risk; improved; improved outcome; learning strategy; markov model; novel; predictive modeling; process repeatability; risk prediction model; sociodemographics; socioeconomics; success; suicidal behavior; suicidal risk; suicide rate; tool

Suicide is the tenth leading cause of death in the United States, accounting for more than 40,000 deaths annually. Despite ongoing efforts to reduce the burden of suicide and suicidal behavior, rates have remained relatively constant over the past half century. Attempts to predict suicidal behavior have relied almost exclusively on self-reporting of suicidal thoughts and intentions. This is problematic because of well-known reporting biases and the fact that many people at high risk are motivated to deny suicidal thoughts to avoid hospitalization. Even though the majority of all suicide decedents have contact with a healthcare professional in the month before their death, suicide risk is rarely detected in such cases. Efforts to identify risk factors have also been stymied by the fact that suicide is a low-base rate event so that very large samples are needed to test the complex combinations of factors that are likely to contribute to risk. The widespread adoption of longitudinal electronic health records (EHRs) has created a powerful but still under-utilized resource for detecting and predicting important health outcomes. In prior work using machine learning methods to analyze structured EHR data, we have developed predictive models that detect up to 45% of first-episode suicidal behavior, on average 3 years in advance. Here we aim to systematically extend and improve our EHR prediction methods in a large healthcare system (N = 4.6 million patients) by incorporating 1) external public record datasets (LexisNexis SocioEconomic Health Attribute data) that include environmental, socioeconomic, and life event information; 2) natural language processing (NLP) to leverage unstructured EHR text, including text-based scores that capture RDoC domains; 3) a novel method of deriving temporal risk envelopes to capture the time-dependent effects of individual risk factors; and 4) clinical risk trajectories that incorporate ordered temporal sequences of risk factors. We will systematically compare the performance of each of these approaches to identify optimal strategies for enhancing risk surveillance and prediction in healthcare settings. Completion of these aims would represent a crucial step towards novel, clinically deployable, and potentially transformative tools for improving outcomes for those at risk for suicide and suicidal behavior.

自杀是美国第十大死亡原因，死亡人数超过4万人 每年。尽管一直在努力减少自杀和自杀行为的负担， 在过去的半个世纪中相对稳定。预测自杀行为的尝试几乎依赖于 专门针对自杀想法和意图的自我报告。这是有问题的，因为众所周知的 报告偏见和事实，许多人在高风险的动机否认自杀的想法，以避免 住院尽管大多数自杀者都与医疗专业人员有过接触 在他们死亡前的一个月内，很少发现这种情况下有自杀的危险。确定风险因素的努力 自杀是一个低基数事件，因此需要非常大的样本来 测试可能导致风险的复杂因素组合。的广泛采用 纵向电子健康记录（EHR）为以下方面创造了一个强大但尚未充分利用的资源： 检测和预测重要的健康结果。在先前的工作中，使用机器学习方法来分析 结构化的EHR数据，我们已经开发出预测模型，检测高达45%的首次发作自杀 行为，平均提前3年。在这里，我们的目标是系统地扩展和改进我们的EHR 大型医疗保健系统（N = 460万患者）中的预测方法，包括1）外部公众 记录数据集（LNEXIS社会经济健康属性数据），包括环境，社会经济， 和生活事件信息; 2）自然语言处理（NLP），以利用非结构化EHR文本，包括 基于文本的分数，捕获RDoC域; 3）一种新的方法，推导时间风险信封， 捕获个体风险因素的时间依赖性影响;以及4）临床风险轨迹， 风险因素的有序时间序列。我们将系统地比较每一个的性能， 确定最佳策略的方法，以加强医疗保健环境中的风险监测和预测。 这些目标的完成将是迈向新的，临床可部署的， 为那些有自杀和自杀行为风险的人改善结果的变革工具。