Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
批准号:
10462646
负责人:
STEVEN C MARCUS
金额:
$72.62万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-08-05 至 2025-05-31
关键词:
Accident and Emergency departmentAddressAlgorithmsArtificial IntelligenceCaringCessation of lifeCharacteristicsClinicalClinical DataClinical ServicesClinical assessmentsComplexComputational algorithmCrisis InterventionDataData AnalyticsData SetData SourcesDatabasesDevelopmentDiagnosisDrug PrescriptionsElectronic Health RecordEmergency CareEmergency Department PhysicianEmergency Department evaluationEmergency MedicineEmergency department visitEventHealthHealthcare SystemsInterventionKnowledgeLinkMachine LearningMedicalMental HealthMental Health ServicesMental disordersMethodsModelingOutcomeOutpatientsOutputPatientsPatternPersonsPhysiciansPreventionProceduresProcessProviderPsychiatristPublic HealthRecording of previous eventsResearchResourcesRiskRisk AssessmentRisk FactorsRisk ManagementServicesSuicideSuicide attemptSuicide preventionSymptomsTechniquesTimeTranslatingUpdateVisitanalytical methodbaseclinical decision supportclinical encounterclinical riskdata miningemergency settingsfollow-uphealth service usehigh riskimprovedindexinginnovationinsightinterestmachine learning methodmachine learning modelmodel developmentprediction algorithmpredictive modelingprototyperisk predictionsuicidalsuicidal behaviorsuicidal morbiditysuicidal patientsuicidal risksupport toolstrait
中文摘要
项目摘要
预防自杀是美国医疗保健面临的重大公共卫生挑战之一
系统寻求精神健康投诉紧急护理的人短期内
非致命性自杀事件和自杀的风险。然而,识别高风险患者具有挑战性,
风险波动的方式知之甚少。评估风险尤其困难,
紧急情况下,获取患者心理健康史的机会往往有限。的
拟议的项目旨在通过配对数据挖掘和
机器学习方法与丰富的数据源,以发展短期预测
精神健康的ED患者的非致命性自杀事件和自杀模型
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
电子健康记录(EHR)数据的技术,以开发临床上丰富的艾德描述
预测自杀和非致命性自杀事件的精神健康患者特征,
天的随访期; 2)使用纵向和时间特征的EHR和索赔数据,
艾德心理健康访视前180天,以产生临床可解释的自杀,
自杀事件风险评分;和3)召集艾德医师以增强模型开发,
临床可解释性和自杀风险评估临床决策支持工具的实用性。
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,可用于告知临床风险评估,
管理向急诊科提出精神健康投诉的患者。风险算法将
使用来自大型组合EHR和索赔的健康信息进行开发和验证
超过2400万商业保险患者的数据集,与全国死亡率相关
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,
有效的计算机算法,这种方法有潜力开发临床解释
在艾德评估时和处置后的自杀风险评分。这将有助于前-
一线临床医生在高风险期将精力集中在高风险患者身上,
关于自杀风险的干预决定。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Administrative Data Transfer Masking, Access, and Ethics Core
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批准号:10774554
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项目类别:
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资助金额:$89.81万
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财政年份:2023
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负责人:STEVEN C MARCUS
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依托单位:
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
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批准号:10277514
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依托单位:
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
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批准号:10631239
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项目类别:
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资助金额:$69.24万
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财政年份:2021
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负责人:STEVEN C MARCUS
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依托单位:
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
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批准号:10809977
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依托单位:
Improving the Emergency Department Management of Deliberate Self-Harm
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项目类别:
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财政年份:2016
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负责人:STEVEN C MARCUS
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依托单位:
Emergency Department Recognition of Mental Disorders and Short-Term Outcome of Deliberate Self-Harm in Older Adults
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批准号:9443725
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项目类别:
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财政年份:2016
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负责人:STEVEN C MARCUS
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Improving the Emergency Department Management of Deliberate Self-Harm
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项目类别:
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财政年份:2016
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负责人:STEVEN C MARCUS
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依托单位:
Inpatient Psychiatric Safety at the VA
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批准号:8597954
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项目类别:
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资助金额:$0.0万
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财政年份:2012
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负责人:STEVEN C MARCUS
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依托单位:
Inpatient Psychiatric Safety at the VA
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资助金额:$0.0万
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财政年份:2012
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依托单位:
Patient Safety in Inpatient Psychiatry
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依托单位:
Patient Safety in Inpatient Psychiatry
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资助金额:$51.35万
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财政年份:2010
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依托单位:
Patient Safety in Inpatient Psychiatry
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资助金额:$56.16万
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财政年份:2010
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依托单位:
Patient Safety in Inpatient Psychiatry
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批准号:8484442
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项目类别:
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资助金额:$43.2万
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财政年份:2010
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负责人:STEVEN C MARCUS
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Understanding Medical Errors in Psychiatry
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依托单位:
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财政年份:--
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负责人:STEVEN C MARCUS
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依托单位:
海外基金