Mining health data for drug safety profiles

描述（由申请人提供）：临床试验，在对照人群中测试药物的安全性和有效性，不能确定与药物相关的所有安全性问题，因为目标人群的规模和特征，使用时间，伴随的疾病状况和治疗在实际使用条件下显着不同。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10万人死亡，成本为1770亿美元。在住院患者方面，据估计，大约30%的住院患者有药物不良事件。目前一次一种药物的监测方法严重不足，因为没有人监测患者在多重合并症的情况下同时服用超过3种药物的“现实生活”情况。在前期工作中，我们建立了一个标注和分析管道，该管道使用公共生物医学本体形成的知识图来进行非结构化临床笔记的数据挖掘。我们已经证明，我们可以从临床记录中复制药物安全信号，平均提前2.7年发布药物安全警报。利用这一管道，我们建议：1)识别和优先考虑值得测试的多药组合；2)开发发现多药联合不良事件概况的方法；3)创建基于电子病历的多药联合潜在不良事件目录。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，通过聚合来改进信号检测，减少多个假设检验，并使多药物副作用的搜索在计算上易于处理。

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.