Biosurveillance/SNOMED-CT Natural Language Processing

生物恐怖主义仍然是对我们公共健康的重大威胁。及早确定与接触生物恐怖主义制剂相一致的患者表现的发生率增加，是遏制生物恐怖袭击和更快地对接触者进行治疗的重要方法。与接触生物恐怖主义制剂相一致的陈述往往在认识到与生物恐怖主义有关的暴露已经发生之前大量出现。这提供了从患者记录中捕获和分析信号的机会。我们建议从临床记录(一节一节)中提供自动化的临床信息提取，这些信息将使用SNOMED-CT编码，这可以作为监测数据的基础。我们认为，这些数据(按临床记录的部分设置的代码)包含有关患者的表现、发现、药物、过敏和合并症的重要和显著的医学事实(代码)，可以从临床记录中提取出来。在这项研究中，我们将分析SNOMED-CT提供与接触毒剂相关的症状星座的足够内容覆盖的能力 生物恐怖主义(即炭疽、天花、蓖麻毒素和辐射暴露)。我们的方法建立在实验室已有的大量研究基础上。自1987年以来，我们一直在研究使用受控医学词汇对医学内容进行编纂的方法。在这项研究中，我们将使用梅奥生物医学信息学实验室开发的梅奥词汇服务器(MVS)，并已在梅奥、约翰·霍普金斯大学和退伍军人管理局医学中心使用，都取得了巨大的成功。我们的MVS工具包的性能已经被验证用于诊断，我们显示出99.7%的灵敏度和97.9%的特异度。 这项提议涉及的实际问题导致了数据的互操作。这项研究的成果将有助于我们的国家倡议，为安全和有效的生物安全生物监测解决方案铺平道路。

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.