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: 8383905
• 负责人: Georgia Tourassi
• 金额: $ 41.05万
• 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/8383905
• 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. PUBLIC HEALTH RELEVANCE: Web mining has emerged in different domains as a powerful approach to harvesting knowledge of unprecedented quantity, comprehensiveness, and diversity. In this study we propose to pursue web mining in the environmental cancer risk domain. We will develop dedicated cyber-informatics algorithms and tools to (i) automatically search disparate online sources for retrieving and integrating contents related to individuals¿ cancer history and spatiotemporal environmental exposure profiles, and to (ii) effectively synthesize this information to accelerate knowledge discovery on environmental cancer risk change due to an individual's migration activities.

描述（由申请人提供）：万维网（Web 1.0）和在线社交媒体（Web 2.0）彻底改变了医学知识的传播方式，以及患者、支持者和医疗保健提供者之间交换和共享健康信息的方式。在线患者社区一直在以令人印象深刻的速度扩大，来自所有年龄组的数百万活跃参与者。最近关于研究和分析与健康有关的应用的社交媒体内容的研究表明，这种上升的网络趋势导致传统上通过观察性流行病学研究等科学方法获得的宝贵知识。这种新的信息获取模式对于需要长时间数据管理的研究特别有利。我们建议利用在线内容的力量，包括社交网站上的用户生成内容，来解决NCI关于复杂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。我们假设，在癌症患者和无癌症人群之间公开分享的大量个人信息可以被有效地挖掘，以产生关于这一主题的新知识，这在我们的现代经济中无法通过传统的移民研究轻易发现，人口流动模式比过去观察到的更加复杂和动态。为了实现我们的目标，我们将在橡树岭国家实验室（ORNL）的超大规模搜索，识别和理解自由结构的网络内容上建立我们独特的网络信息学经验。具体来说，我们将开发特定领域的信息学工具，以自动重建人们的时空生命线，将它们与环境保护局等在线资源提供的时空环境数据联系起来，并使用机器学习方法挖掘它们，以搜索受移民影响的环境暴露变化与癌症风险之间的显著关联。这些工具将被单独验证，整体方法将被仔细测试，以了解其能力，方法学挑战和实际限制（如果有的话）的知识发现和科学探索环境癌症流行病学。这项研究有可能为观察性流行病学研究的标准范式提供一种强有力的补充方法。它将提供一种完全自动化和具有成本效益的方式，以发现新趋势，并监测现代人口移徙模式和环境癌症风险影响的演变趋势。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和优先考虑值得通过仔细控制和适当动力（但也是长期和昂贵的）流行病学研究进行测试的研究假设。 公共卫生关系：Web挖掘已经在不同的领域出现，作为一种强大的方法来收获前所未有的数量，全面性和多样性的知识。在这项研究中，我们建议在环境癌症风险领域进行网络挖掘。我们将开发专用的网络信息学算法和工具，以（i）自动搜索不同的在线资源，以检索和整合与个人癌症史和时空环境暴露概况相关的内容，并（ii）有效地综合这些信息，以加速对个人迁移活动导致的环境癌症风险变化的知识发现。