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: 10223453
• 负责人: Angela Colantonio
• 金额: $ 18.85万
• 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 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10223453
• 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; Ontario; Outcome; Output; Patients; Pattern; Persons; Phenotype; Population; Populations at Risk; Predisposition; Prevalence; Preventive; Probability; Process; Prognosis; Province; Public Health; Recovery; Regenerative capacity; Research; Research Methodology; Research Proposals; Resources; Risk; Risk Factors; 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; personalized medicine; population based; precision medicine; predictive modeling; prevent; prognostic; programs; response; risk stratification; 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（TBI）被认为是世界各地死亡和残疾的主要原因， 国际经济每年约为4000亿美元，根据估计的标准化世界总产值， 73.7万亿美元，占全球全年产出的0.5%。为了解决有关的深刻问题， 在过去的几十年里，急诊科就诊和TBI住院人数急剧增加， 州国会强调伤害监测是联邦的优先事项。美国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，并发出足够的病例概率信号， 疫情爆发，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， 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， 将TBI与其他临床人群区分开来。国家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率、功能和经济负担风险的共患病率：长达十年的研究 一项基于人群的队列研究”是TBI患者存在复杂和多方面的合并症 在受伤前和受伤时，以及它们与患者虚弱、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人群中超过70，000个临床诊断代码进行测序，并将其与匹配人群进行比较。 所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，而且还允许 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 非假设驱动的科学方法的进步，这将：（1）表征TBI患者在三个 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 和种群水平模型来研究不同时间状态之间的转换;（3）构建和验证 TBI事件的易感性、不良结局和个体高医疗资源使用的预测模型 人口水平。公共资助的医疗保健机构数十年来基于人口的卫生管理数据 加拿大安大略系统已准备好进一步分析临床和技术进步，以支持人类 思考对TBI前的个人、临床和环境暴露数据进行分类。

项目成果

Application of multiple testing procedures for identifying relevant comorbidities, from a large set, in traumatic brain injury for research applications utilizing big health-administrative data.

• DOI: 10.3389/fdata.2022.793606
• 发表时间: 2022
• 期刊: FRONTIERS IN BIG DATA
• 影响因子: 3.1
• 作者: Jana, Sayantee;Sutton, Mitchell;Mollayeva, Tatyana;Chan, Vincy;Colantonio, Angela;Escobar, Michael David
• 通讯作者: Escobar, Michael David

Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study.

整合无监督和监督学习技术来预测创伤性脑损伤：一项基于人群的研究。

• DOI: 10.1016/j.ibmed.2023.100118
• 发表时间: 2023
• 期刊: Intelligence-based medicine
• 作者: Zulbayar,Suvd;Mollayeva,Tatyana;Colantonio,Angela;Chan,Vincy;Escobar,Michael
• 通讯作者: Escobar,Michael

The effect of sleep disorders on dementia risk in patients with traumatic brain injury: A large-scale cohort study.

• DOI: 10.1002/dad2.12411
• 发表时间: 2023-04
• 期刊: Alzheimer's & dementia (Amsterdam, Netherlands)

Comorbidity in traumatic brain injury and functional outcomes: a systematic review.

• DOI: 10.23736/s1973-9087.21.06491-1
• 发表时间: 2021-08
• 期刊: EUROPEAN JOURNAL OF PHYSICAL AND REHABILITATION MEDICINE
• 影响因子: 4.5
• 作者: Hanafy, Sara;Xiong, Chen;Chan, Vincy;Sutton, Mitchell;Escobar, Michael;Colantonio, Angela;Mollayeva, Tatyana
• 通讯作者: Mollayeva, Tatyana

Decoding health status transitions of over 200 000 patients with traumatic brain injury from preceding injury to the injury event.

• DOI: 10.1038/s41598-022-08782-0
• 发表时间: 2022-04-04
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Mollayeva T;Tran A;Chan V;Colantonio A;Sutton M;Escobar MD
• 通讯作者: Escobar MD