Nosocomial infections: Automated typing and data mining

描述(申请人提供)：医院感染导致约90,000人 美国每年的死亡人数和相关的医疗费用约为 35亿美元。尽管是第四大死因，但仍有 限制了快速、集成的工具的开发，以确定 医院获得性感染暴发。拟议研究的目标是 测试用于识别的软件算法开发的可行性 与医院感染有关的细菌群。这将是 通过创建新的细菌脂肪酸聚类算法来实现 合成数据以检测感染集群，并通过创建 “数据挖掘”算法为患者提供所需的人口统计信息 区分医院暴发与社区获得性感染或 假性暴发。这些软件算法将被集成到MIDI中 夏洛克微生物全自动实时鉴定系统 流行病学工具。医院感染控制人员将能够使用 产出以立即实施感染控制措施，从而减少 医院感染的影响。 建议的商业应用：不可用

DESCRIPTION (provided by applicant): Nosocomial infections cause about 90,000 deaths annually in the U.S. and have an associated medical care cost of about 3.5 billion dollars. Despite being the fourth leading cause of death, there has been limited development of rapid, integrated tools for determination of outbreaks of hospital-acquired infections. The goal of the proposed research is to test feasibility of development of software algorithms for identifying clusters of bacteria involved in nosocomial infections. This will be accomplished by creation of new algorithms for clustering bacterial fatty acid composition data to detect infection clusters and through the creation of a "data mining" algorithm to provide patient demographic information needed to distinguish nosocomial outbreaks from community-acquired infections or pseudo-outbreaks. These software algorithms will be integrated into the MIDI Sherlock Microbial Identification System as a fully automated real-time epidemiology tool. Hospital infection-control personnel will be able to use the output to immediately implement infection control measures, and thus to reduce the impact of nosocomial infections. PROPOSED COMMERCIAL APPLICATION: NOT AVAILABLE