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一种基于病例的治疗推荐顾问系统(ICONS):重症医学中计算抗生素治疗的计算机辅助建议

A case-based consiliarius for therapy recommendation (ICONS): computer-based advice for calculated antibiotic therapy in intensive care medicine.

作者信息

Heindl B, Schmidt R, Schmid G, Haller M, Pfaller P, Gierl L, Pollwein B

机构信息

Department of Anesthesiology, Klinikum Grosshadern, Ludwig-Maximilians-University Munich, Germany.

出版信息

Comput Methods Programs Biomed. 1997 Feb;52(2):117-27. doi: 10.1016/s0169-2607(96)01789-0.

Abstract

We report here on the system ICONS which utilizes case-based reasoning for medical decision support. As an application domain we have chosen the medical field of 'calculated antibiotic therapy' in an intensive care medicine setting. The system ICONS which runs on a personal computer suggests adequate antibiotic therapy regimen satisfying medical and economic conditions. To speed up the process of finding an adequate antibiotic therapy for a current patient, case-based reasoning is used for finding previously documented similar cases and for modifying them according to the requirements of the current patient. To reduce the memory capacity for the documentation of cases, collections of similar cases are clustered to prototypes. Medical knowledge is represented within a hierarchy of such prototypes and cases and an additional context-sensitive background knowledge-base. A knowledge acquisition tool was programmed that allows revisions of the background medical knowledge-base by simple and comprehensive methods. In addition to the advantage of producing site-specific and time-dependent knowledge, case-based reasoning is a practical method for speeding-up the process of generating and evaluating hypotheses in medical classification tasks.

摘要

我们在此报告ICON系统,该系统利用基于案例的推理进行医疗决策支持。作为应用领域,我们选择了重症监护医学环境中“计算抗生素治疗”的医学领域。在个人计算机上运行的ICON系统会建议符合医学和经济条件的适当抗生素治疗方案。为了加快为当前患者找到适当抗生素治疗方案的过程,基于案例的推理用于查找先前记录的相似案例,并根据当前患者的需求对其进行修改。为了减少案例文档的存储容量,将相似案例的集合聚类为原型。医学知识在这些原型和案例的层次结构以及一个额外的上下文敏感背景知识库中表示。编写了一个知识获取工具,允许通过简单而全面的方法修订背景医学知识库。除了产生特定地点和时间相关知识的优势外,基于案例的推理还是一种在医学分类任务中加快生成和评估假设过程的实用方法。

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