Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims

项目摘要 防止自杀是美国医疗保健面临的最大公共卫生挑战之一 系统。因精神疾病而寻求紧急治疗的人短期内情绪高涨 非致命自杀事件和自杀的风险。然而，识别高危患者是具有挑战性的，因为 风险以一种鲜为人知的方式波动。要评估风险，尤其困难。 急诊设置，在这种情况下，获取患者的心理健康记录往往受到限制。这个 拟议的项目旨在通过将数据挖掘和数据挖掘相结合来解决这一关键的知识差距 具有丰富数据源的机器学习方法，以开发短期预测 精神疾病患者的非致命性自杀事件和自杀模式 有问题。 本研究的具体目的是：1)应用先进的机器学习数据分析 电子健康记录(EHR)数据的技术，以开发临床丰富的ED描述 心理健康患者的特征可以预测90岁以上的自杀和非致命自杀事件- 日跟踪期；2)使用电子病历的纵向和时间特征以及来自 在ED精神健康访问前180天产生临床可解释的自杀和 自杀事件风险评分；以及3)召集急诊医生以加强模型开发， 自杀风险评估临床决策支持工具的临床可解释性和实用性。 我们将通过利用几种不同的复杂机器学习来实现这些目标 现有纵向临床和服务使用信息的分析方法。我们寻求 制定自杀症状和自杀死亡的时间点短期风险评分 驱动该风险的临床特征，可用于为临床风险评估和 对向急诊科提出精神健康问题的病人的管理。风险算法将 使用大型组合电子病历和索赔中的健康信息进行开发和验证 超过2400万名商业保险患者的数据集，这与国家死亡有关 索引。这些发现将对患者特定的风险因素和潜在目标产生新的见解 进行干预。通过利用大多数医疗保健系统通用的数据源并使用 高效的计算机算法这种方法有可能开发出临床可解释的 在ED评估和后续处理时，自杀风险评分。这将有助于正面- LINE临床医生在高危时期将精力集中在高危患者身上，以告知 关于自杀风险的干预决定。

Project Summary Preventing suicide is one of the great public health challenges facing the US health care system. People who seek emergency care for mental health complaints are at high short-term risk of non-fatal suicide events and suicide. Yet identifying high-risk patients is challenging as risk fluctuates in a poorly understood manner. It is especially difficult to evaluate risk in emergency settings, where access to the patient's mental health history is often limited. The proposed project seeks to address this critical knowledge gap by pairing data mining and machine learning methods with rich data sources in order to develop short-term prediction models of non-fatal suicidal events and suicide for patients presenting to EDs with mental health problems. The specific aims of this study are to 1) apply advanced machine learning data analytic techniques to electronic health record (EHR) data to develop a clinically rich description of ED mental health patient characteristics that predict suicide and non-fatal suicidal events over a 90- day follow-up period; 2) use longitudinal and temporal features of EHR and claims data from the 180 days preceding the ED mental health visit to generate clinically interpretable suicide and suicidal event risk scores; and 3) convene ED physicians to enhance model development, clinical interpretability, and utility of a suicide risk assessment clinical decision support tool. We will achieve these aims by leveraging several different sophisticated machine learning analytic methods of existing longitudinal clinical and service use information. We seek to develop point-in-time, short-term risk scores for suicidal symptoms and suicide death and the clinical features that drive that risk that may be used to inform clinical risk assessment and management of patients who present to EDs with mental health complaints. Risk algorithms will be developed and validated using health information from a large combined EHR and claims dataset with over 24 million commercially insured patients, which is linked to the National Death Index. Findings will yield new insights regarding patient-specific risk factors and potential targets for intervention. By drawing on data sources common to most health care systems and using efficient computer algorithms this approach has the potential to develop clinically interpretable suicide risk scores at the point of ED evaluation and following disposition. This will help front- line clinicians focus their efforts on high risk patients during high risk periods to inform intervention decisions about suicide risk.