A lightweight approach for extracting disease-symptom relation with Meta〓toward automated generation of disease knowledge base
A lightweight approach for extracting disease-symptom relation with Meta〓toward automated generation of disease knowledge base
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一种利用Meta〓自动生成疾病知识库来提取疾病症状关系的轻量级方法
DOI：
10.1007/978-3-642-29361-0_20
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
T. Okumura and Y. Tateishi
中科院分区：
文献类型：
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作者：
Shuang Wu;Junpei Kawamoto;Hiroaki Kikuchi;Jun Sakuma;清水正宏，民山浩輔，宮坂恒太，宮坂恒太，中井淳一，大倉正道，細田耕;森裕紀;T. Okumura and Y. Tateishi
诊断决策支持系统需要疾病知识库，这部分在诊断决策支持系统的总开发成本中可能占主导地位。因此，为了实现疾病知识库的自动化生成，我们利用MetaMap（一种将UMLS（统一医学语言系统）语义标签分配到给定医学文献文本中的短语的工具）对症状表达的有效提取进行了初步研究。我们首先利用MetaMap输出中的几个与症状和发现相关的标签来提取症状术语。这种简单的方法导致召回率82%，精度64%。然后，我们应用了一种启发式方法，利用在典型症状表达中经常出现的标签序列的某些模式。这种简单的方法在不牺牲精确度的情况下获得了7%的召回率。虽然提取的信息需要人工检查，但研究表明，简单的方法可以提取症状表达，成本很低。为了进一步提高性能，还对输出进行了故障分析。
Diagnostic decision support systems necessitate disease knowledge base, and this part may occupy dominant portion in the total development cost of such systems. Accordingly, toward automated generation of disease knowledge base, we conducted a preliminary study for efficient extraction of symptomatic expressions, utilizing MetaMap, a tool for assigning UMLS (Unified Medical Language System) semantic tags onto phrases in a given medical literature text.We first utilized several tags in the MetaMap output, related to symptoms and findings, for extraction of symptomatic terms. This straightforward approach resulted in Recall 82% and Precision 64%. Then, we applied a heuristics that exploits certain patterns of tag sequences that frequently appear in typical symptomatic expressions. This simple approach achieved 7% recall gain, without sacrificing precision.Although the extracted information requires manual inspection, the study suggested that the simple approach can extract symptomatic expressions, at very low cost. Failure analysis of the output was also performed to further improve the performance.