Mining health data for drug safety profiles

挖掘健康数据以获取药物安全概况

• 批准号: 8728954
• 负责人: NIGAM H SHAH
• 金额: $ 57.96万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8728954
• 关键词: Adverse drug effect; Adverse effects; Adverse event; Ambulatory Care; Behavior Therapy; Blinded; Cataloging; Catalogs; Cessation of life; Characteristics; Clinical; Clinical Trials; Code; Comorbidity; Computerized Medical Record; Cost Savings; Data; Data Reporting; Data Set; Data Sources; Detection; Development; Dimensions; Disease; Disease Association; Drug Combinations; Drug usage; Electronic Health Record; Event; Graph; Growth; Health; Healthcare; Individual; Inpatients; Knowledge; Label; Length of Stay; Life; Medical Electronics; Methods; Mining; Monitor; Morbidity - disease rate; Natural Language Processing; Ontology; Outpatients; Patients; Pharmaceutical Preparations; Physicians; Population Control; Positioning Attribute; Proxy; Rattus; Reporting; Resources; Safety; Side; Signal Transduction; Source; Surveillance Methods; System; Target Populations; Testing; Text; Time; United States; Validation; Work; aging population; base; biomedical ontology; blind; cost; data mining; drug efficacy; improved; insight; knowledge base; mortality; multiple drug use; novel; patient safety; public health relevance; safety testing; systematic review; tool; trend

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.

描述(由申请人提供)：在受控人群中测试药物安全性和有效性的临床试验，不能确定与药物有关的所有安全问题，因为目标人群的规模和特征、使用时间、伴随的疾病情况和治疗方法在实际使用条件下明显不同。在门诊方面，据估计，在美国，与药物相关的发病率和死亡率每年导致10万人死亡，成本为1770亿美元。在住院方面，据估计，大约30%的住院患者有不良药物事件。目前一次监测一种药物的方法严重不足，因为没有人监测在多种并存情况下服用3种以上伴随药物的患者的“真实生活”情况。在前期工作中，我们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来挖掘非结构化临床笔记。我们已经证明，我们可以在发出药物安全警报之前平均2.7年从临床笔记中重现药物安全信号。利用这一渠道，我们建议：1)识别和优先排序值得试验的多药组合；2)开发发现多药组合不良事件概况的方法；3)创建多药组合潜在不良事件的EHR衍生目录。我们将使用现有的药物、疾病和副作用公共本体提供的层次结构来改进聚合信号检测，减少多重假设检验，并使搜索多药物副作用变得易于计算。