Weakly supervised learning of biomedical information extraction from curated data

BMC Bioinformatics

Proceedings

Tsung-Ting Kuo¹  Chun-Nan Hsu¹  Suvir Jain²  Shitij Bhargava²  Kashyap R.²  Gordon Lin² 

[1] Department of Biomedical Informatics, School of Medicine, University of California, San Diego, 9500 Gilman Drive, 92093, La Jolla, USA;Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, 92093, La Jolla, USA;

关键词: Biomedical text mining;    Natural language processing;    Information extraction;    Database curation;    Machine learning;   

DOI  :  10.1186/s12859-015-0844-1

来源: Springer

【 摘 要 】

BackgroundNumerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exact mentions and their locations in the text required for supervised machine learning. This paper describes a general approach to information extraction using curated data as training examples. The idea is to formulate the problem as cost-sensitive learning from noisy labels, where the cost is estimated by a committee of weak classifiers that consider both curated data and the text.ResultsWe test the idea on two information extraction tasks of Genome-Wide Association Studies (GWAS). The first task is to extract target phenotypes (diseases or traits) of a study and the second is to extract ethnicity backgrounds of study subjects for different stages (initial or replication). Experimental results show that our approach can achieve 87 % of Precision-at-2 (P@2) for disease/trait extraction, and 0.83 of F1-Score for stage-ethnicity extraction, both outperforming their cost-insensitive baseline counterparts.ConclusionsThe results show that curated biomedical databases can potentially be reused as training examples to train information extractors without expert annotation or refinement, opening an unprecedented opportunity of using “big data” in biomedical text mining.

【 授权许可】

Unknown   
© Jain et al. 2015. This article is published under license to BioMed Central Ltd. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.