Lamma Evelina, Mello Paola, Nanetti Anna, Riguzzi Fabrizio, Storari Sergio, Valastro Gianfranco
University of Ferrara, Italy.
IEEE Trans Inf Technol Biomed. 2006 Jan;10(1):143-55. doi: 10.1109/titb.2005.855537.
The monitoring and detection of nosocomial infections is a very important problem arising in hospitals. A hospital-acquired or nosocomial infection is a disease that develops after admission into the hospital and it is the consequence of a treatment, not necessarily a surgical one, performed by the medical staff. Nosocomial infections are dangerous because they are caused by bacteria which have dangerous (critical) resistance to antibiotics. This problem is very serious all over the world. In Italy, almost 5-8% of the patients admitted into hospitals develop this kind of infection. In order to reduce this figure, policies for controlling infections should be adopted by medical practitioners. In order to support them in this complex task, we have developed a system, called MERCURIO, capable of managing different aspects of the problem. The objectives of this system are the validation of microbiological data and the creation of a real time epidemiological information system. The system is useful for laboratory physicians, because it supports them in the execution of the microbiological analyses; for clinicians, because it supports them in the definition of the prophylaxis, of the most suitable antibi-otic therapy and in monitoring patients' infections; and for epidemiologists, because it allows them to identify outbreaks and to study infection dynamics. In order to achieve these objectives, we have adopted expert system and data mining techniques. We have also integrated a statistical module that monitors the diffusion of nosocomial infections over time in the hospital, and that strictly interacts with the knowledge based module. Data mining techniques have been used for improving the system knowledge base. The knowledge discovery process is not antithetic, but complementary to the one based on manual knowledge elicitation. In order to verify the reliability of the tasks performed by MERCURIO and the usefulness of the knowledge discovery approach, we performed a test based on a dataset of real infection events. In the validation task MERCURIO achieved an accuracy of 98.5%, a sensitivity of 98.5% and a specificity of 99%. In the therapy suggestion task, MERCURIO achieved very high accuracy and specificity as well. The executed test provided many insights to experts, too (we discovered some of their mistakes). The knowledge discovery approach was very effective in validating part of the MERCURIO knowledge base, and also in extending it with new validation rules, confirmed by interviewed microbiologists and specific to the hospital laboratory under consideration.
医院感染的监测与检测是医院中出现的一个非常重要的问题。医院获得性感染或医院感染是指患者入院后发生的疾病，它是医护人员进行治疗（不一定是手术治疗）的结果。医院感染很危险，因为它们是由对抗生素具有危险（关键）耐药性的细菌引起的。这个问题在全世界都非常严重。在意大利，近5% - 8%的入院患者会发生这种感染。为了降低这一数字，医生应采取感染控制政策。为了在这项复杂的任务中为他们提供支持，我们开发了一个名为MERCURIO的系统，该系统能够处理该问题的不同方面。该系统的目标是验证微生物学数据并创建一个实时流行病学信息系统。该系统对实验室医生很有用，因为它支持他们进行微生物学分析；对临床医生也很有用，因为它支持他们确定预防措施、最合适的抗生素治疗方案并监测患者的感染情况；对流行病学家同样有用，因为它使他们能够识别疫情爆发并研究感染动态。为了实现这些目标，我们采用了专家系统和数据挖掘技术。我们还集成了一个统计模块，该模块可监测医院中医院感染随时间的传播情况，并与基于知识的模块进行严格交互。数据挖掘技术已用于改进系统知识库。知识发现过程并非与基于人工知识获取的过程相互对立，而是与之互补。为了验证MERCURIO执行任务的可靠性以及知识发现方法的有效性，我们基于真实感染事件的数据集进行了测试。在验证任务中，MERCURIO的准确率为98.5%，灵敏度为98.5%，特异性为99%。在治疗建议任务中，MERCURIO也取得了非常高的准确率和特异性。所执行的测试也为专家提供了许多见解（我们发现了他们的一些错误）。知识发现方法在验证MERCURIO知识库的一部分以及用新的验证规则扩展知识库方面非常有效，这些新规则得到了受访微生物学家的证实，并且特定于所考虑的医院实验室。
