SCH: INT: Mining Drug-Drug Interaction Induced Adverse Effects from Health Record Databases

SCH：INT：从健康记录数据库中挖掘药物相互作用引起的不良反应

• 批准号: 1827472
• 负责人: Li Shen
• 金额: $ 89.84万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827472&HistoricalAwards=false
• 关键词: SCH; INT; Mining; Drug; Interaction

Recent advances in large-scale electronic health record database techniques provide exciting new opportunities to the study of drug safety. Drug-drug interactions (DDIs), a major cause of adverse drug events (ADEs), are a serious global health concern, and a severe detriment to public health. The scale of DDIs involving three or more drugs (also called high-order DDIs) has posed a prohibitory challenge for its molecular pharmacology and clinical research, which motivates alternative strategies such as mining health record data. This project aims to develop large-scale computational strategies and effective software tools for mining high-order DDI effects from health record databases, in order to yield novel discoveries in drug safety, and ultimately to benefit national health and well being. To achieve the above goal, this project is designed to complete four specific tasks. Task 1 aims to develop a novel statistical framework to discover high-order DDI signals associated with ADEs from health record databases. Task 2 aims to study a novel drug safety problem for mining directional DDI signals. Task 3 aims to develop an innovative approach for mining directional DDI patterns at the drug-group level. Task 4 is devoted to software development, evaluation and validation. The project applies these methods to analyze three independent databases, packages method implementations into a user-friendly software toolkit, and releases the toolkit to the public. This project not only facilitates the development of novel computational techniques in drug safety research, but also addresses emerging scientific questions in modeling, mining, and visual exploration of complex data such as the health record data. The project's educational activities include course development, student mentoring and advising, and involvement of minority and underrepresented students in research activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，大规模电子病历数据库技术的发展为药物安全研究提供了令人兴奋的新机遇。药物-药物相互作用（ddi）是药物不良事件（ADEs）的一个主要原因，是一个严重的全球卫生问题，严重损害公共卫生。涉及三种或三种以上药物的ddi的规模（也称为高阶ddi）对其分子药理学和临床研究提出了禁禁性挑战，这促使了挖掘健康记录数据等替代策略。该项目旨在开发大规模计算策略和有效的软件工具，从健康记录数据库中挖掘高阶DDI效应，以便在药物安全方面取得新发现，并最终使国民健康和福祉受益。为了实现上述目标，本项目设计完成四个具体任务。任务1旨在开发一种新的统计框架，从健康记录数据库中发现与ade相关的高阶DDI信号。任务2旨在研究一种新的药物安全问题，用于挖掘定向DDI信号。任务3旨在开发一种在药物组级别挖掘定向DDI模式的创新方法。任务4致力于软件开发、评估和验证。该项目运用这些方法分析了三个独立的数据库，将方法实现打包成一个用户友好的软件工具包，并向公众发布工具包。该项目不仅促进了药物安全研究中新型计算技术的发展，而且还解决了诸如健康记录数据等复杂数据的建模、挖掘和可视化探索方面的新兴科学问题。该项目的教育活动包括课程开发，学生指导和建议，以及少数民族和代表性不足的学生参与研究活动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。