Application of Data Sciences in Traumatic Brain Injury
数据科学在脑外伤中的应用
基本信息
- 批准号:9685513
- 负责人:
- 金额:$ 20.16万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-09-12 至 2020-08-31
- 项目状态:已结题
- 来源:
- 关键词:Absence of pain sensationAcuteAgeAgreementAmnesiaAnesthesia proceduresAnesthesiologyAnestheticsBayesian ModelingBlood PressureBrainCaringCause of DeathCerebral IschemiaCerebrumClinical DataClinical InformaticsComplexDataData ScienceData SetDiagnostic radiologic examinationDoseEpilepsyEventFibrinogenFunctional disorderGoalsHealthcareHourHypotensionHypoxemiaImageInjuryIntracranial PressureMachine LearningMethodsModelingMorbidity - disease rateNatureOperative Surgical ProceduresOpioidOutcomePatient CarePatientsPerformancePerfusionPerioperativePharmaceutical PreparationsPhysiologicalPostoperative CarePostoperative PeriodPrevalencePreventionPreventivePublic HealthQuality of CareRiskSedation procedureSensitivity and SpecificitySeriesTBI PatientsTechniquesTestingTimeTrainingTraumatic Brain InjuryTreatment FactorTreatment outcomebasecare outcomescomparativecomputer based statistical methodsdisabilityhigh riskimplementation scienceimprovedinnovationlearning strategymortalityneurosurgerypatient responsepreventresponsesexvasoactive agent
项目摘要
ABSTRACT
Traumatic brain injury (TBI) is a leading cause of morbidity and mortality, and patients with moderate-severe
traumatic brain TBI often require urgent/emergent surgical and anesthesia care. Patients with TBI who have
surgery have poor outcomes, attributed to a high (>50%) prevalence of perioperative second insults such as
hypotension and hypocarbia, which reduce cerebral perfusion and cause cerebral ischemia. Anesthesiologists
provide analgesia, sedation, immobility, and amnesia, and aim to confer physiological stability, expected
patient response, real-time physiological data, and professional judgement but are unfortunately unable to
accurately predict in real time which patients with TBI will have hypotension and hypocarbia. Yet, avoidance of
these second insults increases discharge survival among patients with TBI. Predicting and preventing
hypotension and hypocarbia during TBI care is, therefore, vital, and avoidance of hypotension and hypocarbia
are key performance indicators for perioperative TBI care. Small data science studies suggest that machine
learning (ML) techniques can model and predict TBI pathophysiology and help reduce unwanted second
insults after TBI. The project goal is to use ML methods to prevent second insults (hypotension and
hypocarbia) during urgent/emergent perioperative TBI care. In response to PA-16-161, we propose 2 Specific
Aims: 1) To construct and identify the TBI physiological ML model that most accurately predicts
perioperative hypotension and hypocarbia, and 2) To develop ML derived personalized prescriptions
for prevention of perioperative hypotension and hypocarbia. This project is innovative and will be
impactful because the approach is grounded in strong data science, and acute care, and implementation
science frameworks, because it develops ML derived prescriptions to prevent hypotension and hypocarbia,
and because we use ML solutions to improve care quality and outcomes after TBI.
摘要
创伤性脑损伤(TBI)是导致发病率和死亡率的主要原因,中-重度脑损伤患者
创伤性脑外伤通常需要紧急手术和麻醉护理。患有脑损伤的患者
手术结果很差,归因于围手术期二次侮辱的发生率很高(50%),例如
低血压和低碳酸血症,减少脑血流,导致脑缺血。麻醉师
提供止痛、镇静、不动和健忘,并旨在提供预期的生理稳定性
患者反应、实时生理数据和专业判断,但不幸的是无法
实时准确预测哪些脑外伤患者会出现低血压和低碳酸血症。然而,避免
这些第二次侮辱增加了脑外伤患者的出院存活率。预测和预防
因此,颅脑损伤护理期间的低血压和低碳酸血症是至关重要的,并可避免低血压和低碳酸血症。
是颅脑损伤围手术期护理的关键绩效指标。小数据科学研究表明,机器
学习(ML)技术可以模拟和预测脑损伤的病理生理学,并帮助减少不必要的秒
在TBI之后的侮辱。该项目的目标是使用ML方法来预防二次侮辱(低血压和
低碳酸血症)在急诊/急诊颅脑损伤围术期护理。针对PA-16-161,我们提出2个具体建议
目的:1)构建和识别最能准确预测的脑损伤生理性ML模型
围手术期低血压和低碳酸血症,以及2)开发ML衍生的个性化处方
用于预防围手术期低血压和低碳酸血症。这个项目是创新的,将是
效果显著,因为该方法以强大的数据科学、急性护理和实施为基础
科学框架,因为它开发ML派生的处方来预防低血压和低碳酸血症,
因为我们使用ML解决方案来改善TBI后的护理质量和结果。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('Su-In Lee', 18)}}的其他基金
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10132962 - 财政年份:2019
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Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets
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- 批准号:
10613437 - 财政年份:2019
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Interpretable Machine Learning to Identify Alzheimer's Disease Therapeutic Targets
可解释的机器学习识别阿尔茨海默病的治疗目标
- 批准号:
10347341 - 财政年份:2019
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Opening the Black Box of Machine Learning Models
打开机器学习模型的黑匣子
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10437684 - 财政年份:2018
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Opening the Black Box of Machine Learning Models
打开机器学习模型的黑匣子
- 批准号:
10224845 - 财政年份:2018
- 资助金额:
$ 20.16万 - 项目类别:
Opening the Black Box of Machine Learning Models
打开机器学习模型的黑匣子
- 批准号:
10020414 - 财政年份:2018
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$ 20.16万 - 项目类别:
Core F: Artificial Intelligence and Bioinformatics
核心F:人工智能和生物信息学
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10260483 - 财政年份:1997
- 资助金额:
$ 20.16万 - 项目类别:
Core F: Artificial Intelligence and Bioinformatics
核心F:人工智能和生物信息学
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10438909 - 财政年份:1997
- 资助金额:
$ 20.16万 - 项目类别:
Core F: Artificial Intelligence and Bioinformatics
核心F:人工智能和生物信息学
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10670111 - 财政年份:1997
- 资助金额:
$ 20.16万 - 项目类别:
Core F: Artificial Intelligence and Bioinformatics
核心F:人工智能和生物信息学
- 批准号:
10042623 - 财政年份:1997
- 资助金额:
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