Mining health data for drug safety profiles

描述（申请人提供）：临床试验在对照人群中检测药物的安全性和有效性，由于目标人群的规模和特征、使用持续时间、伴随疾病和治疗在实际使用条件下存在显著差异，因此无法识别与药物相关的所有安全性问题。 在门诊方面，美国与药物相关的发病率和死亡率估计每年导致100，000人死亡和1770亿美元的费用。 在住院患者方面，据估计，大约30%的住院患者发生了药物不良事件。 目前的一次一种药物的监测方法是远远不够的，因为没有人监测“真实的生活”的情况下，患者获得超过3个合并药物的背景下，多种共病。 在初步工作中，我们已经建立了一个注释和分析管道，使用公共生物医学本体形成的知识图的目的数据挖掘非结构化的临床笔记。 我们已经证明，我们可以在药物安全警报发布前平均2.7年从临床记录中重现药物安全性信号。 使用该管道，我们提出：1）识别和优先化值得测试的多药物组合; 2）开发用于发现多药物组合的不良事件概况的方法;以及3）创建多药物组合的潜在不良事件的EHR衍生目录。 我们将使用现有的药物，疾病和副作用的公共本体提供的层次结构，以提高聚合信号检测，以减少多个假设检验，并使搜索多药物副作用计算上容易处理。

DESCRIPTION (provided by applicant): Clinical trials, which test the safety and efficacy of drugs in a controlled population, cannot identify all safety issues associated with drugs because the size and characteristics of the target population, duration of use, the concomitant disease conditions and therapies differ markedly in actual usage conditions. On the outpatient side, medication related morbidity and mortality in the United States is estimated to result in 100,000 deaths and $177 billion in cost annually. On the inpatient side, it is estimated that roughly 30% of hospital stays have an adverse drug event. Current one-drug-at-a-time methods for surveillance are woefully inadequate because no one monitors the "real life" situation of patients getting over 3 concomitant drugs in the context of multiple co-morbidities. In preliminary work, we have built an annotation and analysis pipeline that uses the knowledge-graph formed by public biomedical ontologies for the purpose data-mining unstructured clinical notes. We have demonstrated that we can reproduce drug safety signals from the clinical notes on average 2.7 years ahead of the issue of a drug safety alert. Using this pipeline, we propose: 1) to identfy and prioritize multi-drug combinations that are worth testing; 2) to develop methods for discovering adverse event profiles of multi- drug combinations; and 3) to create an EHR derived catalogue of potential adverse events of multi-drug combinations. We will use hierarchies provided by existing public ontologies for drugs, diseases and side- effects to improve signal detection by aggregation, to reduce multiple hypothesis testing and to make a search for multi-drug side effects computationally tractable.