Abstract
The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13.Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract
Highlights
The importance of extracting disease related information mapped to a standardized vocabulary is increasing with the yearly increase of published biomedical literature [1]
Medical subject headings (MeSH) terminology was developed by the National Library of Medicine to speed up and increase the precision of biomedical literature retrieval [4]
After comparing to other similar dictionary-based methods, our results suggest that, with the right combination of additional techniques we can significantly improve the performance of the dictionary lookup based disease name normalization (DNORM)
Summary
The importance of extracting disease related information mapped to a standardized vocabulary is increasing with the yearly increase of published biomedical literature [1]. It is revealed that in 2011, over 20 million documents were available in PubMed alone with an average of 4% increase per year with keywords relating to diseases being the second most common user search query [1]. A PubMed query using the keywords ‘disease OR diseases OR disorder OR disorders’ in early 2016 resulted in over 6.5 million documents revealing an average of 6% yearly increase from 2000 to 2014 (Figure 1). Comparable trends can be observed in specific disease categories such as cancer and cardio vascular diseases. Because of this increase in available literature, researchers are faced with the challenge of identifying biomedical documents relevant to them [2,3]. Text mining techniques can be employed to assist in overcoming these challenges [5]
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