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Biosurveillance Utilizing SNOMED-CT Based Natural Language Processing

Biosurveillance Utilizing SNOMED-CT Based Natural Language Processing
利用基于 SNOMED-CT 的自然语言处理进行生物监测
批准号:
7790142
负责人:
PETER L. ELKIN
金额:
$67.73万
依托单位国家:
美国
项目类别:
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-09-30 至 2010-03-29

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中文摘要
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英文摘要
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.
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