Machine learning to inform health services and policy for traumatic brain injury

机器学习为创伤性脑损伤的医疗服务和政策提供信息

基本信息

批准号：
10030705
负责人：
Angela Colantonio
金额：
$ 18.59万
依托单位：
UNIVERSITY OF TORONTO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10030705
关键词：
Acute Address Affect Ambulances Americas Amnesia Area Behavioral Big Data Biological Brain Brain Injuries Brain Pathology Canada Cardiovascular Diseases Categories Cause of Death Centers for Disease Control and Prevention (U.S.)Characteristics Classification Clinical Code Cohort Studies Complex Congresses Data Decision Support Systems Development Diagnosis Disease Disease Outbreaks Elements Emergency department visit Environmental Exposure Evaluation Event Explosion Exposure to Financial Hardship Funding Gender Genomics Goals Head Health Health Policy Health Services Healthcare Healthcare Systems Hospitalization Human Human Resources Individual Individual Differences Injury International International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)Investments Knowledge Learning Link Machine Learning Medicine Metabolic Modeling Musculoskeletal System Natural regeneration Ontario Outcome Output Patients Pattern Persons Phenotype Population Populations at Risk Predisposition Prevalence Preventive Probability Process Province Public Health Recovery Research Research Methodology Research Proposals Resources Risk Risk Factors Risk stratification Role Secondary to Services Severities Signal Transduction Standardization Stratification Symptoms System TBI Patients Thinking Time Training Translating Traumatic Brain Injury Unconscious State United States United States National Institutes of Health Validation Woman adverse outcome assault behavioral/social science clinical Diagnosis comorbidity cost data mining disability expectation falls frailty functional outcomes gender disparity improved informatics tool injury recovery injury surveillance interest medically necessary care men mortality mortality risk novel outcome forecast personalized medicine population based precision medicine predictive modeling prevent prognostic programs response sex social survivorship vehicular accident virtual

项目摘要

Project Summary Traumatic brain injury (TBI) is recognized as the leading cause of death and disability in all parts of the world and costs the international economy approximately US$400 billion annually, which, given an estimated standardized gross world product of US $73.7 trillion, is a striking 0.5% of the entire annual global output. To address the profound issues related to a drastic increase in emergency department visits and hospitalizations for TBI over the past decades, the United States Congress highlighted injury surveillance as a federal priority. The Centers for Disease Control and Prevention defines surveillance as “use of health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response”. To prevent TBI, it is essential to understand its distribution and patterns, in addition to having strong knowledge of clinical disorders, characteristic, or other definable entity, that differentiates TBI from other clinical populations. A critical barrier to the progress of the NIH-funded program “Comorbidity in traumatic brain injury and risk of all-cause mortality, functional and financial burden: a decade-long population based cohort study” was the presence of complex and multifaceted comorbidities in a patient with TBI before and at the time of the injury, and their links to patients’ frailty, injury circumstances, severity, and outcomes. This resulted in a shift in the research paradigm, and development of a novel data mining approach used in genomics to sequence more than 70,000 clinical diagnosis codes in a TBI population, and compare them to a matched population. The developed data mining approach allowed not only the validation of previously known risk factors of TBI, but also the identification of associations previously unknown, without any preconceived human biases. This project will continue advancement of a non-hypothesis driven scientific approach, which will: (1) Characterize patients with TBI at three different time periods in relation to the TBI event – before, at the time of, and after the injury; (2) Develop individual and population level models to study the transitions between the different time states; and (3) Construct and validate predictive models of susceptibility to TBI events, adverse outcomes, and high healthcare resource use at the individual and population level. Decades- long population-based health administrative data from the publicly-funded healthcare system in Ontario, Canada is ready to be further analysed for clinical and technological advancement, to support human thinking in categorizing personal, clinical, and environmental exposure data preceding TBI.

项目摘要创伤性脑损伤（TBI）被认为是世界各地的死亡和残疾的主要原因，成本国际经济每年约4000亿美元，鉴于估计的标准化总体 73.7万亿美元的产品是全球年度产出的0.5％。解决有关的深刻问题在过去的几十年中，急诊室就诊和TBI住院的大幅增加，联合州国会强调伤害监视是联邦优先事项。疾病控制与预防中心将监视定义为“使用与诊断之前的健康相关数据的使用，并表示案件的可能性足够的可能性或为了防止TBI，必须了解其分布和模式除了对临床疾病，特征或其他可定义的实体了解强烈的知识之外将TBI与其他临床人群区分开来。 NIH资助计划的进步的关键障碍 “脑部创伤性脑损伤和全因死亡率，功能性和金融伯恩的合并症：长达十年基于人群的队列研究”是患有TBI的患者的复杂和多方面合并症的存在受伤前后，及其与患者脆弱，受伤情况，严重程度和结果的联系。这导致研究范式的变化，并开发了一种用于基因组学中的新型数据挖掘方法在TBI人群中，序列超过70,000个临床诊断代码，并将其与匹配的人群进行比较。开发的数据挖掘方法不仅允许验证先前已知的TBI风险因素，还允许鉴定以前未知的关联，没有任何先入为主的人类偏见。这个项目将继续非肢体驱动科学方法的进步，该方法将：（1）在三个处于TBI患者的特征与TBI事件有关的不同时间段 - 受伤之前，之后和之后；（2）发展个体和人口水平模型，以研究不同时间状态之间的过渡；（3）构建和验证对个人TBI事件的易感性，不良结果和高度医疗保健资源使用的预测模型和人口水平。来自公共资助的医疗保健的数十年来基于人群的卫生行政数据加拿大安大略省的系统准备进一步分析临床和技术进步，以支持人类在TBI之前对个人，临床和环境暴露数据进行分类的思考。