Pharmacovigilence using Natural Language Processing, Statistics, and the EHR

描述(由申请人提供)： 这项建议的长期目标是通过使用自动化方法发现新的不良药物事件(ADE)，从而提高患者的安全并降低医疗保健成本。我们将利用自然语言处理(NLP)和数据挖掘方法对电子健康记录(EHR)中的大量临床数据进行检测，以检测新的ADE信号。ADS是世界范围内的主要问题，导致住院、死亡，并导致巨大的医疗费用。因此，包括大量和多样化的患者群体的持续上市后监测对患者安全至关重要。EHR包含全面的临床信息，如果利用得当，对药物警戒将是无价的。我们已经证明，我们可以使用NLP系统MedLEE准确地对临床报告中的信息进行编码，并且我们可以使用我们开发的统计方法准确地检测临床事件之间的关联。因此，这是一个极好的机会，可以继续我们的研究成果，提高药物警戒的最新水平。 更具体地说，MedLEE将用于将EHR中的综合临床信息映射到编码数据，然后将使用统计方法生成关于疾病-症状、疾病-药物、药物-药物和药物-症状关联的广泛知识库，这些知识库将用于发现新的ADE。此外，我们将开发确定药物、疾病和症状事件的正确序列的方法，这对检测ADE至关重要。我们还将开发将细粒度概念映射到更高级别概念的方法，这对于优化统计方法非常重要。我们的发现方法的性能将通过使用当前与已知ADE一起使用的药物进行测试，以及通过使用历史回滚来评估。我们将首先使用住院记录发现短期事件，然后使用门诊访问来发现较长期的事件。 这项提议能够很好地克服现有的以自发报告数据库和行政数据库为基础的自动化方法所带来的问题。我们有信心这些方法将是有效的，因为我们有强大的基础设施供我们建设。最重要的是，这项提案中开发的方法提供了一个极好的机会，可以显著提高患者的安全并降低成本。

DESCRIPTION (provided by applicant): The long-term objective of this proposal is to advance patient safety and reduce the cost of medical care by discovering novel adverse drug events (ADEs) through use of automated methods. We will utilize natural language processing (NLP) and data mining methodologies on vast quantities of clinical data in electronic health records (EHRs) to detect novel ADE signals. ADEs are major problems world-wide and cause hospitalizations, deaths, and incur a huge cost to health care. Therefore, continued post-marketing surveillance encompassing large and varied patient populations is crucial for patient safety. EHRs contain a comprehensive amount of clinical information, which if harnessed properly, would be invaluable for pharmacovigilance. We have already demonstrated that we can accurately encode information in clinical reports using the NLP system MedLEE, and that we can accurately detect associations among clinical events using statistical methods that we developed. Therefore, this is an excellent opportunity to continue our research accomplishments and to advance the state of the art in pharmacovigilance. More specifically, MedLEE will be used to map comprehensive clinical information in the EHR to codified data, and then statistical methods will be used to generate an extensive knowledge base of disease-symptom, disease-drug, drug-drug, and drug-symptom associations, which will be used to discover new ADEs. Additionally, we will develop methods to determine the correct sequence of drug, disease, and symptom events, which is critical for detecting ADEs. We will also develop methods to map fine-grained concepts into higher level concepts, which is important for optimizing the statistical methods. The performance of our discovery methods will be evaluated by testing the methods using drugs currently in use with known ADEs, and also by using historical rollback. We will first focus on discovery of short-term events using inpatient records, and then longer-term events using outpatient office visits. This proposal is well positioned to overcome problems associated with existing automated methods based on spontaneous reporting databases and administrative databases. We are confident the methods will be effective because a strong infrastructure is in place for us to build upon. Most importantly, the methodology developed in this proposal presents an excellent chance to dramatically improve patient safety and reduce costs.