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.

项目概要 创伤性脑损伤 (TBI) 被认为是世界各地死亡和残疾的主要原因 国际经济每年约 4000 亿美元，根据估计的标准化世界总量 产值 73.7 万亿美元，占全球年总产值的 0.5%，令人震惊。解决相关深刻问题 过去几十年来，因 TBI 导致急诊就诊和住院的人数急剧增加，美国 州议会强调伤害监测是联邦的优先事项。疾病控制和预防中心 将监测定义为“在诊断之前使用与健康相关的数据，并表明病例或病例有足够的可能性” 爆发，需要采取进一步的公共卫生应对措施”。为了预防 TBI，必须了解其分布和情况 模式，除了对临床疾病、特征或其他可定义实体有深入的了解之外， TBI 与其他临床人群不同。 NIH 资助项目进展的一个关键障碍 “创伤性脑损伤的合并症以及全因死亡、功能和经济负担的风险：长达十年的 基于人群的队列研究”是 TBI 患者存在复杂且多方面的合并症 受伤前和受伤时，以及它们与患者的虚弱程度、受伤情况、严重程度和结果的联系。这 导致了研究范式的转变，并开发了一种用于基因组学的新型数据挖掘方法 对 TBI 人群中 70,000 多个临床诊断代码进行测序，并将其与匹配人群进行比较。 开发的数据挖掘方法不仅可以验证先前已知的 TBI 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。该项目将继续进行 推进非假设驱动的科学方法，该方法将：（1）在三个阶段描述 TBI 患者的特征 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 和人口水平模型来研究不同时间状态之间的转变； (3)构建并验证 predictive models of susceptibility to TBI events, adverse outcomes, and high healthcare resource use at the individual 和人口水平。来自公共资助的医疗保健机构长达数十年的基于人口的健康管理数据 加拿大安大略省的系统已准备好进一步分析临床和技术进步，以支持人类 对 TBI 之前的个人、临床和环境暴露数据进行分类的思考。