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

使用电子健康记录和索赔的机器学习急诊科自杀预测算法的开发和临床解释

• 批准号: 10809977
• 负责人: STEVEN C MARCUS
• 金额: $ 30.33万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10809977
• 关键词: Accident and Emergency department; Address; Algorithms; Artificial Intelligence; Caring; Cessation of life; Characteristics; Clinical; Clinical Data; Complex; Computational algorithm; Crisis Intervention; Data; Data Analytics; Data Set; Data Sources; Databases; Development; Diagnosis; Drug Prescriptions; Electronic Health Record; Emergency Care; Emergency Department Physician; Emergency Department evaluation; Emergency Medicine; Emergency department visit; Event; Health; Healthcare Systems; Intervention; Knowledge; Link; Machine Learning; Medical; Mental Health; Mental Health Services; Mental disorders; Methods; Modeling; Organizational Models; Outcome; Outpatients; Output; Patients; Pattern; Persons; Physicians; Prevention; Procedures; Process; Provider; Psychiatrist; Public Health; Recording of previous events; Research; Resources; Risk; Risk Assessment; Risk Factors; Risk Management; Services; Suicide; Suicide attempt; Suicide prevention; Symptoms; Techniques; Time; Translating; Update; Visit; analytical method; clinical decision support; clinical encounter; clinical risk; data mining; emergency settings; follow-up; health service use; high risk; improved; indexing; innovation; insight; interest; machine learning method; machine learning model; model development; prediction algorithm; predictive modeling; prototype; risk prediction; suicidal; suicidal behavior; suicidal morbidity; suicidal patient; suicidal risk; support tools; trait

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.

项目概要 预防自杀是美国医疗保健面临的重大公共卫生挑战之一 系统。因心理健康问题寻求紧急护理的人短期处于高水平 非致命性自杀事件和自杀的风险。然而，识别高危患者具有挑战性，因为 风险以一种人们知之甚少的方式波动。评估风险尤其困难 在紧急情况下，了解患者的心理健康史往往受到限制。这 拟议的项目旨在通过将数据挖掘和 具有丰富数据源的机器学习方法，以开发短期预测 非致命性自杀事件和心理健康患者自杀模型 问题。 本研究的具体目标是 1）应用先进的机器学习数据分析 技术对电子健康记录 (EHR) 数据进行丰富的临床描述 预测 90 岁以上自杀和非致命自杀事件的心理健康患者特征 日间随访期； 2) 使用 EHR 的纵向和时间特征以及来自 急诊科心理健康就诊前 180 天产生临床上可解释的自杀 自杀事件风险评分； 3) 召集急诊科医生加强模型开发， 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。我们力求 制定自杀症状和自杀死亡的时间点、短期风险评分 驱动风险的临床特征可用于指导临床风险评估和 对向急诊室提出心理健康问题的患者进行管理。风险算法将 使用来自大型综合电子病历和索赔的健康信息进行开发和验证 包含超过 2400 万商业保险患者的数据集，与全国死亡相关 指数。研究结果将产生关于患者特定风险因素和潜在目标的新见解 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潜力开发出临床可解释的 急诊室评估时和处置后的自杀风险评分。这将有助于前端 一线临床医生在高风险时期将重点放在高风险患者身上，以告知 关于自杀风险的干预决策。