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      Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion

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          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

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          NCBI disease corpus: a resource for disease name recognition and concept normalization.

          Zhiyong Lu, Rezarta Islamaj Doğan, Robert Leaman (2014)
          Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora. This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/. Published by Elsevier Inc.
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            DNorm: disease name normalization with pairwise learning to rank

            Robert Leaman, Rezarta Islamaj Doğan, Zhiyong Lu (2013)
            Motivation: Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text—the task of disease name normalization (DNorm)—compared with other normalization tasks in biomedical text mining research. Methods: In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. Results: We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. Availability: The source code for DNorm is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator Contact: zhiyong.lu@nih.gov
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              PubMed and beyond: a survey of web tools for searching biomedical literature

              Zhiyong Lu (2011)
              The past decade has witnessed the modern advances of high-throughput technology and rapid growth of research capacity in producing large-scale biological data, both of which were concomitant with an exponential growth of biomedical literature. This wealth of scholarly knowledge is of significant importance for researchers in making scientific discoveries and healthcare professionals in managing health-related matters. However, the acquisition of such information is becoming increasingly difficult due to its large volume and rapid growth. In response, the National Center for Biotechnology Information (NCBI) is continuously making changes to its PubMed Web service for improvement. Meanwhile, different entities have devoted themselves to developing Web tools for helping users quickly and efficiently search and retrieve relevant publications. These practices, together with maturity in the field of text mining, have led to an increase in the number and quality of various Web tools that provide comparable literature search service to PubMed. In this study, we review 28 such tools, highlight their respective innovations, compare them to the PubMed system and one another, and discuss directions for future development. Furthermore, we have built a website dedicated to tracking existing systems and future advances in the field of biomedical literature search. Taken together, our work serves information seekers in choosing tools for their needs and service providers and developers in keeping current in the field. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/search
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                Author and article information

                Journal
                Journal ID (nlm-ta): Database (Oxford)
                Journal ID (iso-abbrev): Database (Oxford)
                Journal ID (publisher-id): databa
                Journal ID (hwp): databa
                Title: Database: The Journal of Biological Databases and Curation
                Publisher: Oxford University Press
                ISSN (Electronic): 1758-0463
                Publication date Collection: 2016
                Publication date (Electronic): 08 August 2016
                Publication date PMC-release: 08 August 2016
                Volume: 2016
                Electronic Location Identifier: baw112
                Affiliations
                1School of Public Health and Community Medicine, UNSW, Kensington, NSW 2033, Australia
                2Prince of Wales Clinical School, UNSW, Kensington, NSW 2033, Australia
                3Institution of Information Science, Academia Sinica, Taipei 115, Taiwan
                4Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan and
                5Department of Computer Science and Information Engineering, National Taitung University, Taipei, Taiwan
                Author notes

                Citation details: Jonnagaddala,J., Jue,T.R., Chang,N.-W. and Dai,H.-J. Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion. Database (2016) Vol. 2016: article ID baw112; doi:10.1093/database/baw112

                Article
                Publisher ID: baw112
                DOI: 10.1093/database/baw112
                PMC ID: 4976299
                PubMed ID: 27504009
                SO-VID: 3566c14f-1c78-4b6d-986b-061ceb25a0f0
                Copyright © © The Author(s) 2016. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 05 December 2015
                Date revision received : 05 July 2016
                Date accepted : 06 July 2016
                Page count
                Pages: 14
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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