Next-Generation Safety Data Mining System

描述（由申请人提供）：本研究的总体目标是开发一种先进的数据挖掘环境，用于从不良反应报告的大型数据库中提取与药物（和其他治疗产品）安全性相关的信息。该项目建立在对第一代系统的先前研究的基础上，该系统已在FDA，CDC和几家制药公司进行了试点使用。这一快速通道提案的重点是创建一个下一代系统，沿着几个方面取得重大突破：计算和统计方法，易用性和易访问性，数据预处理和后处理，以及结果可视化和解释。这种下一代系统是一种改进风险管理的技术，包括早期安全信号和药物相互作用和不良事件综合征的调查。 在第一阶段，将通过四个关键领域的研究来确定可行性：（1）性能增强，以处理更大，更复杂的问题;（2）结果过滤，以帮助区分“有趣”和“无趣”的结果;（3）用于比较研究的不同基线;以及（4）用于探索和解释两个以上因素影响的专门图形。第二阶段将创建拟议的下一代安全数据挖掘系统的全尺寸原型，并将包括由协作的统计人员和医疗安全干事进行的几个周期的β测试和验证。

DESCRIPTION (provided by applicant): The overall aim of this research is to develop an advanced data mining environment for extracting information relating to the safety of drugs (and other therapeutic products) from large databases of adverse reaction reports. The project builds upon prior research on a first-generation system which has received pilot use at FDA, CDC, and several pharmaceutical companies. This Fast Track proposal focuses on creating a next-generation system that achieves significant breakthroughs along several dimensions: computational and statistical methodology, ease-of-use and ease-of-access, data pre- and post-processing, and results visualization and interpretation. Such a next-generation system is an enabling technology for improved risk management, including early safety signaling and the investigation of drug interactions and adverse event syndromes. In Phase I, feasibility will be established through research in four key areas: (1) performance enhancement to handle larger, more complex problems; 2) results filtering to help with discriminating "interesting" from "uninteresting" results; 3) different baselines for use in comparison studies; and 4) specialized graphics for exploring and interpreting more than two-factor effects. Phase II will create a full-scale prototype of the proposed next-generation safety data mining system and will include several cycles of beta-testing and validation by collaborating statisticians and medical safety officers.