Answering clinical questions with knowledge-based and statistical techniques
Answering clinical questions with knowledge-based and statistical techniques
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DOI：
10.1162/coli.2007.33.1.63
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发表时间：
2007-03-01
影响因子：
9.3
通讯作者：
Lin, Jimmy
中科院分区：
文献类型：
--
作者：
Demner-Fushman, Dina;Lin, Jimmy
问答研究的最新发展与专为医学领域文本的自动语义处理开发的无与伦比的资源的可用性相结合，为探索临床医学领域中的复杂问题回答提供了独特的机会。本文介绍了一个为满足循证医学医师的信息需求而设计的系统。我们已经开发了一系列知识抽取器，它们结合了基于知识和统计的技术，用于自动识别MEDLINE摘要的临床相关方面。根据循证医学的原则，这些提取的元素用作算法的输入，该算法根据信息需求的结构化表示对引文的相关性进行评分。从PubMed检索到的初始引用列表开始，我们的系统可以将相关摘要放到更高的排名位置，并从这些摘要生成直接回答医生问题的响应。我们描述了三个独立的评估：一个集中在知识抽取器的准确性上，一个被概念化为文件重新排序任务，最后是两个医生对答案的评估。对一组真实世界的临床问题进行的实验表明，我们的方法显著优于已经具有竞争力的PubMed基线。
The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline.