Generating disease-pertinent treatment vocabularies from MEDLINE citations.
Generating disease-pertinent treatment vocabularies from MEDLINE citations.
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DOI：
10.1016/j.jbi.2016.11.004
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发表时间：
2017-01
影响因子：
4.5
通讯作者：
Haug PJ
中科院分区：
文献类型：
--
作者：
Wang L;Del Fiol G;Bray BE;Haug PJ
医疗保健社区已经确定了对疾病特异性信息的重大需求。疾病特异性本体在辅助从各种源检索疾病相关信息方面是有用的。然而，构建这些本体是劳动密集型的。我们的目标是开发一个系统，用于从流行的知识资源中自动生成疾病相关的概念，用于构建疾病特异性本体。开发了一个管道系统，其初始重点是生成疾病特异性治疗词汇表。它由疾病特异性引文检索、谓词提取、治疗谓词提取、治疗概念提取和相关性排名组成。开发了一个语义模式，以支持治疗预测和概念的提取。四种排序方法（即，发生率、兴趣、程度中心性和加权程度中心性）来测量治疗概念与感兴趣疾病的相关性。我们测量了四个等级的表现，在前100个概念的平均精度与五种疾病，以及精度召回曲线对两个参考词汇。还将该系统的性能与两种基线方法进行了比较。管道系统实现了前100个概念的平均精度为0.80，按兴趣排名。四个等级之间无显著差异（p = 0.53）。然而，基于流水线的系统的性能明显优于两个基线。管道系统可以是有用的疾病相关的治疗概念，从生物医学文献的自动生成。
Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p = 0.53). However, the pipeline-based system had significantly better performance than the two baselines. The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.
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影响因子：
2.7
作者：
Nebot, Victoria;Berlanga, Rafael
通讯作者：
Berlanga, Rafael
影响因子：
39.2
作者：
COVELL, DG;UMAN, GC;MANNING, PR
通讯作者：
MANNING, PR
使用中心性在文献挖掘的基因互动网络上识别基因 - 疾病的关联。
DOI：
10.1093/bioinformatics/btn182
发表时间：
2008-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ozgür A;Vu T;Erkan G;Radev DR
通讯作者：
Radev DR
影响因子：
105.7
作者：
Haynes, RB;Wilczynski, NL
通讯作者：
Wilczynski, NL
DOI：
10.1136/jamia.1994.95153434
发表时间：
1994-11-01
影响因子：
6.4
作者：
HAYNES, RB;WILCZYNSKI, N;SINCLAIR, JC
通讯作者：
SINCLAIR, JC