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

• 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
• 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

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 创伤性脑损伤在世界各地被公认为死亡和残疾的主要原因，并造成了巨大的损失。 国际经济每年约4000亿美元，考虑到估计的标准化世界总值， 73.7万亿美元的产值，占全球年产值的0.5%，令人震惊。解决相关的深层次问题 在过去的几十年里，由于脑损伤急诊科就诊和住院人数的急剧增加，美国 各州国会强调，伤害监测是联邦政府的优先事项。疾病控制和预防中心 监测的定义是“在诊断之前使用与健康有关的数据，并发出足够的病例或 暴发需要采取进一步的公共卫生应对措施“。要预防脑损伤，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有很强的了解外， 区别于其他临床人群的TBI。美国国立卫生研究院资助项目进展的关键障碍 创伤性脑损伤与全因死亡风险、功能和经济负担的共病：长达十年 基于人群的队列研究“是指脑外伤患者存在复杂和多方面的合并症 在受伤之前和受伤时，以及它们与患者的虚弱、受伤情况、严重程度和结果的联系。这 导致研究范式的转变，并开发了一种用于基因组学的新的数据挖掘方法，以 将7万多个临床诊断代码在一个脑损伤人群中进行测序，并将它们与匹配的人群进行比较。 开发的数据挖掘方法不仅可以验证先前已知的脑外伤风险因素，还可以验证 识别以前未知的关联，没有任何先入为主的人类偏见。这一项目将继续下去 一种非假说驱动的科学方法的进展，它将：(1)将脑外伤患者的特征定为3 与TBI事件相关的不同时间段--受伤前、受伤时和受伤后；(2)发展个人 和种群水平模型，用于研究不同时间状态之间的转换；以及(3)构建和验证 个体对脑外伤事件、不良后果和高卫生保健资源使用的易感性的预测模型 和人口水平。来自公费医疗的数十年基于人口的卫生管理数据 加拿大安大略省的系统准备对临床和技术进步进行进一步分析，以支持人类 在TBI前对个人、临床和环境暴露数据进行分类的思考。