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Liao Y, Liu H, Spasić I. Fine-tuning coreference resolution for different styles of clinical narratives. J Biomed Inform 2024; 149:104578. [PMID: 38122841 DOI: 10.1016/j.jbi.2023.104578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/22/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE Coreference resolution (CR) is a natural language processing (NLP) task that is concerned with finding all expressions within a single document that refer to the same entity. This makes it crucial in supporting downstream NLP tasks such as summarization, question answering and information extraction. Despite great progress in CR, our experiments have highlighted a substandard performance of the existing open-source CR tools in the clinical domain. We set out to explore some practical solutions to fine-tune their performance on clinical data. METHODS We first explored the possibility of automatically producing silver standards following the success of such an approach in other clinical NLP tasks. We designed an ensemble approach that leverages multiple models to automatically annotate co-referring mentions. Subsequently, we looked into other ways of incorporating human feedback to improve the performance of an existing neural network approach. We proposed a semi-automatic annotation process to facilitate the manual annotation process. We also compared the effectiveness of active learning relative to random sampling in an effort to further reduce the cost of manual annotation. RESULTS Our experiments demonstrated that the silver standard approach was ineffective in fine-tuning the CR models. Our results indicated that active learning should also be applied with caution. The semi-automatic annotation approach combined with continued training was found to be well suited for the rapid transfer of CR models under low-resource conditions. The ensemble approach demonstrated a potential to further improve accuracy by leveraging multiple fine-tuned models. CONCLUSION Overall, we have effectively transferred a general CR model to a clinical domain. Our findings based on extensive experimentation have been summarized into practical suggestions for rapid transferring of CR models across different styles of clinical narratives.
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Affiliation(s)
- Yuxiang Liao
- School of Computer Science and Informatics, Cardiff University, United Kingdom.
| | - Hantao Liu
- School of Computer Science and Informatics, Cardiff University, United Kingdom.
| | - Irena Spasić
- School of Computer Science and Informatics, Cardiff University, United Kingdom.
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2
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Kersloot MG, van Putten FJP, Abu-Hanna A, Cornet R, Arts DL. Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies. J Biomed Semantics 2020; 11:14. [PMID: 33198814 PMCID: PMC7670625 DOI: 10.1186/s13326-020-00231-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 11/03/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts to it. However, implementations of NLP algorithms are not evaluated consistently. Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations. METHODS Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included. Year, country, setting, objective, evaluation and validation methods, NLP algorithms, terminology systems, dataset size and language, performance measures, reference standard, generalizability, operational use, and source code availability were extracted. The studies' objectives were categorized by way of induction. These results were used to define recommendations. RESULTS Two thousand three hundred fifty five unique studies were identified. Two hundred fifty six studies reported on the development of NLP algorithms for mapping free text to ontology concepts. Seventy-seven described development and evaluation. Twenty-two studies did not perform a validation on unseen data and 68 studies did not perform external validation. Of 23 studies that claimed that their algorithm was generalizable, 5 tested this by external validation. A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed. CONCLUSION We found many heterogeneous approaches to the reporting on the development and evaluation of NLP algorithms that map clinical text to ontology concepts. Over one-fourth of the identified publications did not perform an evaluation. In addition, over one-fourth of the included studies did not perform a validation, and 88% did not perform external validation. We believe that our recommendations, alongside an existing reporting standard, will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.
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Affiliation(s)
- Martijn G. Kersloot
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
- Castor EDC, Amsterdam, The Netherlands
| | - Florentien J. P. van Putten
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
| | - Ronald Cornet
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
| | - Derk L. Arts
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
- Castor EDC, Amsterdam, The Netherlands
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3
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Liu C, Peres Kury FS, Li Z, Ta C, Wang K, Weng C. Doc2Hpo: a web application for efficient and accurate HPO concept curation. Nucleic Acids Res 2020; 47:W566-W570. [PMID: 31106327 PMCID: PMC6602487 DOI: 10.1093/nar/gkz386] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/26/2019] [Accepted: 04/30/2019] [Indexed: 01/18/2023] Open
Abstract
We present Doc2Hpo, an interactive web application that enables interactive and efficient phenotype concept curation from clinical text with automated concept normalization using the Human Phenotype Ontology (HPO). Users can edit the HPO concepts automatically extracted by Doc2Hpo in real time, and export the extracted HPO concepts into gene prioritization tools. Our evaluation showed that Doc2Hpo significantly reduced manual effort while achieving high accuracy in HPO concept curation. Doc2Hpo is freely available at https://impact2.dbmi.columbia.edu/doc2hpo/. The source code is available at https://github.com/stormliucong/doc2hpo for local installation for protected health data.
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Affiliation(s)
- Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | | | - Ziran Li
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Casey Ta
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Kai Wang
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
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4
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Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10062157] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.
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5
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Vos RA, Katayama T, Mishima H, Kawano S, Kawashima S, Kim JD, Moriya Y, Tokimatsu T, Yamaguchi A, Yamamoto Y, Wu H, Amstutz P, Antezana E, Aoki NP, Arakawa K, Bolleman JT, Bolton E, Bonnal RJP, Bono H, Burger K, Chiba H, Cohen KB, Deutsch EW, Fernández-Breis JT, Fu G, Fujisawa T, Fukushima A, García A, Goto N, Groza T, Hercus C, Hoehndorf R, Itaya K, Juty N, Kawashima T, Kim JH, Kinjo AR, Kotera M, Kozaki K, Kumagai S, Kushida T, Lütteke T, Matsubara M, Miyamoto J, Mohsen A, Mori H, Naito Y, Nakazato T, Nguyen-Xuan J, Nishida K, Nishida N, Nishide H, Ogishima S, Ohta T, Okuda S, Paten B, Perret JL, Prathipati P, Prins P, Queralt-Rosinach N, Shinmachi D, Suzuki S, Tabata T, Takatsuki T, Taylor K, Thompson M, Uchiyama I, Vieira B, Wei CH, Wilkinson M, Yamada I, Yamanaka R, Yoshitake K, Yoshizawa AC, Dumontier M, Kosaki K, Takagi T. BioHackathon 2015: Semantics of data for life sciences and reproducible research. F1000Res 2020; 9:136. [PMID: 32308977 PMCID: PMC7141167 DOI: 10.12688/f1000research.18236.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/05/2020] [Indexed: 01/08/2023] Open
Abstract
We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.
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Affiliation(s)
- Rutger A. Vos
- Institute of Biology Leiden, Leiden University, Leiden, The Netherlands
- Naturalis Biodiversity Center, Leiden, The Netherlands
| | | | - Hiroyuki Mishima
- Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shin Kawano
- Database Center for Life Science, Tokyo, Japan
| | | | | | - Yuki Moriya
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Hongyan Wu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Erick Antezana
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nobuyuki P. Aoki
- Faculty of Science and Engineering, SOKA University, Tokyo, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Jerven T. Bolleman
- SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Lausanne, Switzerland
| | - Evan Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Raoul J. P. Bonnal
- Istituto Nazionale Genetica Molecolare, Romeo ed Enrica Invernizzi, Milan, Italy
| | | | - Kees Burger
- Dutch Techcentre for Life Sciences, Utrecht, The Netherlands
| | - Hirokazu Chiba
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Kevin B. Cohen
- Computational Bioscience Program, University of Colorado School of Medicine, Denver, USA
- Université Paris-Saclay, LIMSI, CNRS, Paris, France
| | | | | | - Gang Fu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | | | | | | | - Naohisa Goto
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Tudor Groza
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, Australia
| | - Colin Hercus
- Novocraft Technologies Sdn. Bhd., Selangor, Malaysia
| | - Robert Hoehndorf
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Kotone Itaya
- Institute for Advanced Biosciences, Keio University, Tokyo, Japan
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Jee-Hyub Kim
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Akira R. Kinjo
- Institute for Protein Research, Osaka University, Osaka, Japan
| | - Masaaki Kotera
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Kouji Kozaki
- The Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan
| | | | - Tatsuya Kushida
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Giessen, Germany
- Gesellschaft für innovative Personalwirtschaftssysteme mbH (GIP GmbH), Offenbach, Germany
| | | | | | - Attayeb Mohsen
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Hiroshi Mori
- Center for Information Biology, National Institute of Genetics, Mishima, Japan
| | - Yuki Naito
- Database Center for Life Science, Tokyo, Japan
| | | | | | | | - Naoki Nishida
- Department of Systems Science, Osaka University, Osaka, Japan
| | - Hiroyo Nishide
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tazro Ohta
- Database Center for Life Science, Tokyo, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, USA
| | | | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
| | - Pjotr Prins
- University Medical Center Utrecht, Utrecht, The Netherlands
- University of Tennessee Health Science Center, Memphis, USA
| | - Núria Queralt-Rosinach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Shinya Suzuki
- School of Life Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Tsuyosi Tabata
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
| | | | - Kieron Taylor
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Mark Thompson
- Leiden University Medical Center, Leiden, The Netherlands
| | - Ikuo Uchiyama
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Japan
| | - Bruno Vieira
- WurmLab, School of Biological & Chemical Sciences, Queen Mary University of London, London, UK
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Mark Wilkinson
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Kazutoshi Yoshitake
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
| | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - Toshihisa Takagi
- National Bioscience Database Center, Japan Science and Technology Agency, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
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6
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Ju M, Short AD, Thompson P, Bakerly ND, Gkoutos GV, Tsaprouni L, Ananiadou S. Annotating and detecting phenotypic information for chronic obstructive pulmonary disease. JAMIA Open 2020; 2:261-271. [PMID: 31984360 PMCID: PMC6951876 DOI: 10.1093/jamiaopen/ooz009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/21/2019] [Accepted: 03/19/2019] [Indexed: 12/29/2022] Open
Abstract
Objectives Chronic obstructive pulmonary disease (COPD) phenotypes cover a range of lung abnormalities. To allow text mining methods to identify pertinent and potentially complex information about these phenotypes from textual data, we have developed a novel annotated corpus, which we use to train a neural network-based named entity recognizer to detect fine-grained COPD phenotypic information. Materials and methods Since COPD phenotype descriptions often mention other concepts within them (proteins, treatments, etc.), our corpus annotations include both outermost phenotype descriptions and concepts nested within them. Our neural layered bidirectional long short-term memory conditional random field (BiLSTM-CRF) network firstly recognizes nested mentions, which are fed into subsequent BiLSTM-CRF layers, to help to recognize enclosing phenotype mentions. Results Our corpus of 30 full papers (available at: http://www.nactem.ac.uk/COPD) is annotated by experts with 27 030 phenotype-related concept mentions, most of which are automatically linked to UMLS Metathesaurus concepts. When trained using the corpus, our BiLSTM-CRF network outperforms other popular approaches in recognizing detailed phenotypic information. Discussion Information extracted by our method can facilitate efficient location and exploration of detailed information about phenotypes, for example, those specifically concerning reactions to treatments. Conclusion The importance of our corpus for developing methods to extract fine-grained information about COPD phenotypes is demonstrated through its successful use to train a layered BiLSTM-CRF network to extract phenotypic information at various levels of granularity. The minimal human intervention needed for training should permit ready adaption to extracting phenotypic information about other diseases.
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Affiliation(s)
- Meizhi Ju
- National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK
| | - Andrea D Short
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Paul Thompson
- National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK
| | - Nawar Diar Bakerly
- Salford Royal NHS Foundation Trust; and School of Health Sciences, The University of Manchester, Manchester, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.,MRC Health Data Research UK (HDR UK).,NIHR Experimental Cancer Medicine Centre, Birmingham, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, UK.,NIHR Biomedical Research Centre, Birmingham, UK
| | - Loukia Tsaprouni
- School of Health Sciences, Centre for Life and Sport Sciences, Birmingham City University, Birmingham, UK
| | - Sophia Ananiadou
- National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK
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7
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Foster M, Pandey A, Kreimeyer K, Botsis T. Generation of an annotated reference standard for vaccine adverse event reports. Vaccine 2018; 36:4325-4330. [PMID: 29880244 DOI: 10.1016/j.vaccine.2018.05.079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 05/08/2018] [Accepted: 05/21/2018] [Indexed: 01/24/2023]
Abstract
As part of a collaborative project between the US Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention for the development of a web-based natural language processing (NLP) workbench, we created a corpus of 1000 Vaccine Adverse Event Reporting System (VAERS) reports annotated for 36,726 clinical features, 13,365 temporal features, and 22,395 clinical-temporal links. This paper describes the final corpus, as well as the methodology used to create it, so that clinical NLP researchers outside FDA can evaluate the utility of the corpus to aid their own work. The creation of this standard went through four phases: pre-training, pre-production, production-clinical feature annotation, and production-temporal annotation. The pre-production phase used a double annotation followed by adjudication strategy to refine and finalize the annotation model while the production phases followed a single annotation strategy to maximize the number of reports in the corpus. An analysis of 30 reports randomly selected as part of a quality control assessment yielded accuracies of 0.97, 0.96, and 0.83 for clinical features, temporal features, and clinical-temporal associations, respectively and speaks to the quality of the corpus.
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Affiliation(s)
- Matthew Foster
- FDA Center for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology. 10903 New Hampshire Ave, Silver Spring, MD, United States.
| | - Abhishek Pandey
- FDA Center for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology. 10903 New Hampshire Ave, Silver Spring, MD, United States
| | - Kory Kreimeyer
- FDA Center for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology. 10903 New Hampshire Ave, Silver Spring, MD, United States
| | - Taxiarchis Botsis
- FDA Center for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology. 10903 New Hampshire Ave, Silver Spring, MD, United States; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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8
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Dahdul W, Manda P, Cui H, Balhoff JP, Dececchi TA, Ibrahim N, Lapp H, Vision T, Mabee PM. Annotation of phenotypes using ontologies: a gold standard for the training and evaluation of natural language processing systems. Database (Oxford) 2018; 2018:5255130. [PMID: 30576485 PMCID: PMC6301375 DOI: 10.1093/database/bay110] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/22/2018] [Accepted: 09/24/2018] [Indexed: 11/12/2022]
Abstract
Natural language descriptions of organismal phenotypes, a principal object of study in biology, are abundant in the biological literature. Expressing these phenotypes as logical statements using ontologies would enable large-scale analysis on phenotypic information from diverse systems. However, considerable human effort is required to make these phenotype descriptions amenable to machine reasoning. Natural language processing tools have been developed to facilitate this task, and the training and evaluation of these tools depend on the availability of high quality, manually annotated gold standard data sets. We describe the development of an expert-curated gold standard data set of annotated phenotypes for evolutionary biology. The gold standard was developed for the curation of complex comparative phenotypes for the Phenoscape project. It was created by consensus among three curators and consists of entity-quality expressions of varying complexity. We use the gold standard to evaluate annotations created by human curators and those generated by the Semantic CharaParser tool. Using four annotation accuracy metrics that can account for any level of relationship between terms from two phenotype annotations, we found that machine-human consistency, or similarity, was significantly lower than inter-curator (human-human) consistency. Surprisingly, allowing curatorsaccess to external information did not significantly increase the similarity of their annotations to the gold standard or have a significant effect on inter-curator consistency. We found that the similarity of machine annotations to the gold standard increased after new relevant ontology terms had been added. Evaluation by the original authors of the character descriptions indicated that the gold standard annotations came closer to representing their intended meaning than did either the curator or machine annotations. These findings point toward ways to better design software to augment human curators and the use of the gold standard corpus will allow training and assessment of new tools to improve phenotype annotation accuracy at scale.
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Affiliation(s)
| | - Prashanti Manda
- University of North Carolina at Greensboro, Greensboro, NC, USA
| | - Hong Cui
- University of Arizona, Tucson, AZ, USA
| | - James P Balhoff
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - T Alexander Dececchi
- University of South Dakota, Vermillion, SD, USA
- Current affiliation: University of Pittsburgh at Johnstown, Johnstown, PA, USA
| | - Nizar Ibrahim
- University of Chicago, Chicago, IL, USA
- Current affiliation: University of Detroit Mercy, Detroit, MI, USA & University of Portsmouth, Portsmouth, UK
| | | | - Todd Vision
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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9
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Henderson J, Bridges R, Ho JC, Wallace BC, Ghosh J. PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2017; 2017:149-157. [PMID: 28815124 PMCID: PMC5543339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
As the adoption of Electronic Healthcare Records has grown, the need to transform manual processes that extract and characterize medical data into automatic and high-throughput processes has also grown. Recently, researchers have tackled the problem of automatically extracting candidate phenotypes from EHR data. Since these phenotypes are usually generated using unsupervised or semi-supervised methods, it is necessary to examine and validate the clinical relevance of the generated "candidate" phenotypes. We present PheKnow-Cloud, a framework that uses co-occurrence analysis on the publicly available, online repository ofjournal articles, PubMed, to build sets of evidence for user-supplied candidate phenotypes. PheKnow-Cloud works in an interactive manner to present the results of the candidate phenotype analysis. This tool seeks to help researchers and clinical professionals evaluate the automatically generated phenotypes so they may tune their processes and understand the candidate phenotypes.
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10
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Alnazzawi N, Thompson P, Ananiadou S. Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource. PLoS One 2016; 11:e0162287. [PMID: 27643689 PMCID: PMC5028053 DOI: 10.1371/journal.pone.0162287] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2016] [Accepted: 08/19/2016] [Indexed: 02/02/2023] Open
Abstract
Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of structures. This variability stems partly from the different backgrounds of the authors, but also from the different writing styles typically used in each text type. Since EHR narrative reports and literature articles contain different but complementary types of valuable information, combining details from each text type can help to uncover new disease-phenotype associations. However, the alternative ways in which the same concept may be mentioned in each source constitutes a barrier to the automatic integration of information. Accordingly, identification of the unique concepts represented by phrases in text can help to bridge the gap between text types. We describe our development of a novel method, PhenoNorm, which integrates a number of different similarity measures to allow automatic linking of phenotype concept mentions to known concepts in the UMLS Metathesaurus, a biomedical terminological resource. PhenoNorm was developed using the PhenoCHF corpus—a collection of literature articles and narratives in EHRs, annotated for phenotypic information relating to congestive heart failure (CHF). We evaluate the performance of PhenoNorm in linking CHF-related phenotype mentions to Metathesaurus concepts, using a newly enriched version of PhenoCHF, in which each phenotype mention has an expert-verified link to a concept in the UMLS Metathesaurus. We show that PhenoNorm outperforms a number of alternative methods applied to the same task. Furthermore, we demonstrate PhenoNorm’s wider utility, by evaluating its ability to link mentions of various other types of medically-related information, occurring in texts covering wider subject areas, to concepts in different terminological resources. We show that PhenoNorm can maintain performance levels, and that its accuracy compares favourably to other methods applied to these tasks.
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Affiliation(s)
- Noha Alnazzawi
- National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester University, Manchester, United Kingdom
- * E-mail:
| | - Paul Thompson
- National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester University, Manchester, United Kingdom
| | - Sophia Ananiadou
- National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester University, Manchester, United Kingdom
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Jonnagaddala J, Jue TR, Chang NW, Dai HJ. Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion. Database (Oxford) 2016; 2016:baw112. [PMID: 27504009 PMCID: PMC4976299 DOI: 10.1093/database/baw112] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2015] [Revised: 07/05/2016] [Accepted: 07/06/2016] [Indexed: 01/01/2023]
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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Affiliation(s)
- Jitendra Jonnagaddala
- School of Public Health and Community Medicine, UNSW, Kensington, NSW 2033, Australia Prince of Wales Clinical School, UNSW, Kensington, NSW 2033, Australia
| | - Toni Rose Jue
- Prince of Wales Clinical School, UNSW, Kensington, NSW 2033, Australia
| | - Nai-Wen Chang
- Institution of Information Science, Academia Sinica, Taipei 115, Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan and
| | - Hong-Jie Dai
- Department of Computer Science and Information Engineering, National Taitung University, Taipei, Taiwan
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Connolly N, Anixt J, Manning P, Ping-I Lin D, Marsolo KA, Bowers K. Maternal metabolic risk factors for autism spectrum disorder-An analysis of electronic medical records and linked birth data. Autism Res 2016; 9:829-37. [PMID: 26824581 DOI: 10.1002/aur.1586] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 11/05/2015] [Accepted: 11/07/2015] [Indexed: 01/12/2023]
Abstract
Past studies have suggested that conditions experienced by women during pregnancy (e.g. obesity and gestational diabetes mellitus (GDM)) may be associated with having a child with autism spectrum disorder (ASD). Our objective was to compare mothers who had a child diagnosed with ASD to mothers of children with a non-ASD developmental disorder (DD) or without any reported DD (controls). To accomplish the objective we collected medical record data from patients who resided in the Cincinnati Children's Hospital Medical Center's (CCHMC) primary catchment area and linked those data to data from birth certificates (to identify risk factors). Two comparison groups were analyzed; one with DD; and the other, controls without a reported ASD or DD. Descriptive statistics and regression analyses evaluated differences. Differences were greater comparing mothers of ASD to controls than comparing ASD to DD. Maternal obesity and GDM were associated with a statistically significant approximately 1.5-fold increased odds of having a child with an ASD. For mothers with both GDM and obesity, the association was twofold for having a child with ASD compared with controls. Maternal obesity and GDM might be associated with an increased risk of ASD in the offspring; however, no difference in risk of ASD according to BMI and GDM was seen when comparing to DD. Autism Res 2016, 9: 829-837,. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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Affiliation(s)
- Natalia Connolly
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Julia Anixt
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Patty Manning
- Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Daniel Ping-I Lin
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Keith A Marsolo
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Katherine Bowers
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio
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Collier N, Oellrich A, Groza T. Concept selection for phenotypes and diseases using learn to rank. J Biomed Semantics 2015; 6:24. [PMID: 26034558 PMCID: PMC4450611 DOI: 10.1186/s13326-015-0019-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 04/01/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Phenotypes form the basis for determining the existence of a disease against the given evidence. Much of this evidence though remains locked away in text - scientific articles, clinical trial reports and electronic patient records (EPR) - where authors use the full expressivity of human language to report their observations. RESULTS In this paper we exploit a combination of off-the-shelf tools for extracting a machine understandable representation of phenotypes and other related concepts that concern the diagnosis and treatment of diseases. These are tested against a gold standard EPR collection that has been annotated with Unified Medical Language System (UMLS) concept identifiers: the ShARE/CLEF 2013 corpus for disorder detection. We evaluate four pipelines as stand-alone systems and then attempt to optimise semantic-type based performance using several learn-to-rank (LTR) approaches - three pairwise and one listwise. We observed that whilst overall Apache cTAKES tended to outperform other stand-alone systems on a strong recall (R = 0.57), precision was low (P = 0.09) leading to low-to-moderate F1 measure (F1 = 0.16). Moreover, there is substantial variation in system performance across semantic types for disorders. For example, the concept Findings (T033) seemed to be very challenging for all systems. Combining systems within LTR improved F1 substantially (F1 = 0.24) particularly for Disease or syndrome (T047) and Anatomical abnormality (T190). Whilst recall is improved markedly, precision remains a challenge (P = 0.15, R = 0.59).
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Affiliation(s)
- Nigel Collier
- University of Cambridge, Cambridge, UK ; European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | - Tudor Groza
- School of ITEE, the University of Queensland, St. Lucia, Australia
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