Pasche Emilie, Teodoro Douglas, Gobeill Julien, Ruch Patrick, Lovis Christian
Medical Informatics Service, University Hospitals of Geneva and University of Geneva.
AMIA Annu Symp Proc. 2009 Nov 14;2009:509-13.
We propose a question-answering (QA) driven generation approach for automatic acquisition of structured rules that can be used in a knowledge authoring tool for antibiotic prescription guidelines management.
The rule generation is seen as a question-answering problem, where the parameters of the questions are known items of the rule (e.g. an infectious disease, caused by a given bacterium) and answers (e.g. some antibiotics) are obtained by a question-answering engine.
When looking for a drug given a pathogen and a disease, top-precision of 0.55 is obtained by the combination of the Boolean engine (PubMed) and the relevance-driven engine (easyIR), which means that for more than half of our evaluation benchmark at least one of the recommended antibiotics was automatically acquired by the rule generation method.
These results suggest that such an automatic text mining approach could provide a useful tool for guidelines management, by improving knowledge update and discovery.
我们提出一种基于问答驱动的生成方法，用于自动获取结构化规则，这些规则可用于抗生素处方指南管理的知识创作工具中。
规则生成被视为一个问答问题，其中问题的参数是规则的已知项（例如，由特定细菌引起的传染病），答案（例如，某些抗生素）由问答引擎获得。
在给定病原体和疾病的情况下寻找药物时，布尔引擎（PubMed）和相关性驱动引擎（easyIR）的组合获得了0.55的最高精度，这意味着在我们超过一半的评估基准中，规则生成方法至少自动获取了一种推荐的抗生素。
这些结果表明，这种自动文本挖掘方法可以通过改进知识更新和发现，为指南管理提供一个有用的工具。
