BMExpert: Mining MEDLINE for Finding Experts in Biomedical Domains Based on Language Model

• 馬見塚, 拓

  Fudan University

• Chen, Xiaodong

  Fudan University

• Mamitsuka, Hiroshi

  Kyoto University

• Zhu, Shanfeng

  Fudan University

抄録

With the rapid development of biomedical sciences, a great number of documents have been published to report new scientific findings and advance the process of knowledge discovery. By the end of 2013, the largest biomedical literature database, MEDLINE, has indexed over 23 million abstracts. It is thus not easy for scientific professionals to find experts on a certain topic in the biomedical domain. In contrast to the existing services that use some ad hoc approaches, we developed a novel solution to biomedical expert finding, BMExpert, based on the language model. For finding biomedical experts, who are the most relevant to a specific topic query, BMExpert mines MEDLINE documents by considering three important factors: relevance of documents to the query topic, importance of documents, and associations between documents and experts. The performance of BMExpert was evaluated on a benchmark dataset, which was built by collecting the program committee members of ISMB in the past three years (2012-2014) on 14 different topics. Experimental results show that BMExpert outperformed three existing biomedical expert finding services: JANE, GoPubMed, and eTBLAST, with respect to both MAP (mean average precision) and P@50 (Precision). BMExpert is freely accessed at http://datamining-iip.fudan.edu.cn/service/BMExpert/.

収録刊行物

• IEEE/ACM Transactions on Computational Biology and Bioinformatics

  IEEE/ACM Transactions on Computational Biology and Bioinformatics 12 (6), 1286-1294, 2015-05-06

  Institute of Electrical and Electronics Engineers Inc. (IEEE)

キーワード

• information retrieval
• Biomedical text mining
• expert finding
• language model

詳細情報

• CRID: 1050570796182934144
• NII論文ID: 120006654074
• ISSN: 15455963
• HANDLE: 2433/218418
• 本文言語コード: en
• 資料種別: conference paper
• データソース種別:
  • IRDB
  • CiNii Articles