A Cyber-Informatics Approach to Studying Migration and Environmental Cancer Risk

研究移民和环境癌症风险的网络信息学方法

• 批准号: 8549183
• 负责人: Georgia Tourassi
• 金额: $ 37.79万
• 依托单位: UT-BATTELLE, LLC-OAK RIDGE NATIONAL LAB
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-21 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8549183
• 关键词: Algorithms; Area; Cancer Patient; Communities; Complex; Data; Data Collection; Data Set; Diagnosis; Disease; Education; Environmental Exposure; Environmental Risk Factor; Epidemiologic Studies; Epidemiologist; Epidemiology; Exposure to; Family; Goals; Harvest; Health; Health Personnel; Health Policy; High Performance Computing; Incidence; Individual; Individual Cancer History; Informatics; Internet; Knowledge; Knowledge Discovery; Laboratories; Link; Location; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Medical; Medical History; Methodology; Methods; Mining; Mission; Modeling; Monitor; Nomads; Participant; Patients; Pattern; Phase; Policy Maker; Population; Predisposition; Prevalence; Prevention; Process; Research; Resources; Risk Factors; Socioeconomic Status; Source; Structure; Technology; Testing; Time; United States Environmental Protection Agency; age group; base; cancer diagnosis; cancer epidemiology; cancer risk; cancer therapy; cost; cost effective; cyber security; data acquisition; design; environment related cancer; environmental carcinogenesis; experience; improved; malignant breast neoplasm; meetings; migration; novel; population migration; social; social networking website; spatiotemporal; tool; trend; web site

DESCRIPTION (provided by applicant): The World-Wide Web (Web 1.0) and online social media (Web 2.0) have revolutionized the ways medical knowledge is disseminated and health information is exchanged and shared among patients, supporters, and health care providers. Online patient communities have been expanding at an impressive rate with millions of active participants from all age groups. Recent studies on researching and analyzing social media contents for health-related applications show that this uprising cyber-trend leads to valuable knowledge, traditionally acquired with scientific methods such as observational epidemiological studies. This new mode for information acquisition is particularly advantageous for studies requiring long period of data curation. We propose to leverage the power of online contents, including user-generated contents on social network sites, to tackle NCI¿s second provocative question on complex migration patterns and their effect on environmental cancer risk. We hypothesize that the rich amount of personal information shared openly among cancer patients and cancer-free people online can be effectively mined to generate new knowledge on the topic, which cannot be easily uncovered with conventional migrant studies in our modern economy with population mobility patterns far more complex and dynamic than those observed in the past. To achieve our goal, we will build upon our unique cyber-informatics experience at the Oak Ridge National Laboratory (ORNL) on ultra-scale searching, identifying, and understanding free-structured web content. Specifically, we will develop domain-specific informatics tools to automatically reconstruct people's spatiotemporal lifelines, link them to spatiotemporal environmental data available from online sources such as the Environmental Protection Agency, and mine them using machine learning methods to search for salient associations between changes of migration-influenced environmental exposure and cancer risk. These tools will be individually validated and the overall approach will be carefully tested to understand its capabilities, methodological challenges, and practical limitations (if any) for knowledge discovery and scientific explorations in environmental cancer epidemiology. This study has the potential to provide a powerful complementary approach to the standard paradigm of observational epidemiological research. It will offer a fully automated and cost-effective way to discover new trends and monitor evolving ones on the impact of modern population migration patterns and environmental cancer risk. Such information could help cancer epidemiologists and health policy makers generate and prioritize study hypotheses worth testing with carefully controlled and properly powered (but also long term and costly) epidemiological studies.

描述（由申请人提供）：万维网 (Web 1.0) 和在线社交媒体 (Web 2.0) 彻底改变了患者、支持者和医疗保健提供者之间传播医学知识以及交换和共享健康信息的方式。患者社区正在以令人印象深刻的速度扩张，拥有来自各个年龄段的数百万活跃参与者。最近对健康相关应用的社交媒体内容进行研究和分析的研究表明，这种新兴的网络趋势带来了传统上获得的宝贵知识。这种新的信息获取模式对于需要长期数据管理的研究尤其有利，我们建议利用在线内容（包括社交网站上用户生成的内容）的力量来应对 NCI。 ¿关于复杂的迁移模式及其对环境癌症风险的影响的第二个挑衅性问题我们发现，癌症患者和无癌症人群在网上公开共享的大量个人信息可以被有效地挖掘以产生有关该主题的新知识，而这是不可能的。在我们现代经济中，人口流动模式比过去观察到的更加复杂和动态，很容易通过传统的移民研究来发现。为了实现我们的目标，我们将利用我们在橡树岭国家实验室（ORNL）独特的网络信息学经验。 ) ) 在超大规模搜索上，具体来说，我们将开发特定领域的信息学工具来自动重建人们的时空生命线，将其与环境保护局等在线来源提供的时空环境数据联系起来，并使用机器学习来挖掘它们。寻找受移民影响的环境暴露变化与癌症风险之间显着关联的方法将对这些工具进行单独验证，并对总体方法进行仔细测试，以了解其能力、方法学挑战和知识的实际局限性（如果有）。这项研究有可能为观察流行病学研究的标准范式提供强有力的补充方法，它将提供一种完全自动化且成本不断变化的方法来发现新趋势并监测其影响。这些信息可以帮助癌症流行病学家和卫生政策制定者产生并优先考虑值得通过仔细控制和适当动力（但也是长期且昂贵的）流行病学研究进行检验的研究假设。