Biosurveillance Utilizing SNOMED-CT Based Natural Language Processing

生物恐怖主义仍然是对我们公共卫生的重大威胁。及早查明儿童死亡率增加的情况 发生与暴露于生物恐怖主义制剂一致的患者表现是一个重要的 控制生物恐怖袭击的方法，并对受影响的个人进行更迅速的治疗。经常 与暴露于生物恐怖主义制剂相一致的情况在 认识到已经发生了与生物恐怖主义相关的暴露。这提供了一个机会， 分析病人记录中的信号 我们建议从临床记录中提供自动化临床信息的摘要（第 将使用SNOMED-CT进行编码，可作为监测数据的基础。我们 我认为，这些数据（按临床记录部分划分的代码集）具有重要和突出的意义， 关于患者表现、发现、药物、过敏和合并症的医学事实（代码） 可以从临床记录中提取。在这项研究中，我们将分析SNOMED-CT的能力， 与暴露于以下物质相关的症状群的充分内容覆盖 生物恐怖主义（即炭疽、天花、蓖麻毒素和辐射暴露）。 我们的方法建立在我们实验室已有的大量研究基础上。我们有 自1987年以来，一直在研究使用受控医学词汇编纂医学内容的方法。 在这项研究中，我们将使用马约词汇服务器（MVS）开发的马约实验室， 生物医学信息学，并已在马约，约翰霍普金斯大学和退伍军人管理局医疗中心使用 都取得了巨大的成功。我们的MVS工具包的性能2已经过诊断验证， 敏感性为99.7%，特异性为97.9%。 这个建议涉及的实际问题，导致了数据的互操作性。这方面的成果 研究将有助于我们的国家倡议，为安全有效的生物安全铺平道路。 生物监测解决方案。

Bioterrorism remains a significant threat to our public health. Early identification of an increased rate of occurance of patient presentations consistent with an exposure to an agent of bioterrorism is one important method to contain bioterror attacks and effect more rapid treatment of exposed individuals. Often presentations consistent with an exposure to an agent of bioterrorism occur in significant numbers prior to the recognition that a bioterrorism related exposure has occurred. This presents an opportunity to capture and analyze signals from patient records. We propose to provide an abstraction of automated clinical information from the clinical record (section by section) that will be coded using SNOMED-CT which can serve as the substrate for surveillance data. We believe that this data (sets of codes by section of the clinical record) which holds the important and salient medical facts (codes) regarding the patients' presentation, findings, medications, allergies and co-morbidities could be abstracted from clinical records. In this study, we will analyze SNOMED-CT's ability to provide adequate content coverage for constellations of symptoms associated with exposures to agents of bioterrorism (i.e. Anthrax, Small Pox, Ricin, and Radiation exposure). Our method builds on the considerable body of research already available within our laboratory. We have been researching methods for codifying medical content using controlled medical vocabularies since 1987. For this study, we will employ the Mayo Vocabulary Server (MVS) developed in the Mayo Laboratory of Biomedical Informatics and has been used at Mayo, Johns Hopkins University and the VA medical centers all with great success. Our performanc2 of the MVS toolkit has been validated for diagnoses where we showed a sensitivity of 99.7% and a specificity of 97.9%. This proposal deals with practical issues that lead the way toward interoperable data. The fruits of this research will assist our national initiatives to pave the way toward a safe and effective BioSecure biosurveillance solution.