Cargando…
PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature
BACKGROUND: Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for es...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188713/ https://www.ncbi.nlm.nih.gov/pubmed/35690873 http://dx.doi.org/10.1186/s13326-022-00272-6 |
_version_ | 1784725428630454272 |
---|---|
author | Binkheder, Samar Wu, Heng-Yi Quinney, Sara K. Zhang, Shijun Zitu, Md. Muntasir Chiang, Chien‐Wei Wang, Lei Jones, Josette Li, Lang |
author_facet | Binkheder, Samar Wu, Heng-Yi Quinney, Sara K. Zhang, Shijun Zitu, Md. Muntasir Chiang, Chien‐Wei Wang, Lei Jones, Josette Li, Lang |
author_sort | Binkheder, Samar |
collection | PubMed |
description | BACKGROUND: Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. RESULTS: Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. CONCLUSIONS: The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13326-022-00272-6. |
format | Online Article Text |
id | pubmed-9188713 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91887132022-06-13 PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature Binkheder, Samar Wu, Heng-Yi Quinney, Sara K. Zhang, Shijun Zitu, Md. Muntasir Chiang, Chien‐Wei Wang, Lei Jones, Josette Li, Lang J Biomed Semantics Research BACKGROUND: Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. RESULTS: Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. CONCLUSIONS: The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13326-022-00272-6. BioMed Central 2022-06-11 /pmc/articles/PMC9188713/ /pubmed/35690873 http://dx.doi.org/10.1186/s13326-022-00272-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Binkheder, Samar Wu, Heng-Yi Quinney, Sara K. Zhang, Shijun Zitu, Md. Muntasir Chiang, Chien‐Wei Wang, Lei Jones, Josette Li, Lang PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature |
title | PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature |
title_full | PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature |
title_fullStr | PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature |
title_full_unstemmed | PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature |
title_short | PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature |
title_sort | phenodef: a corpus for annotating sentences with information of phenotype definitions in biomedical literature |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188713/ https://www.ncbi.nlm.nih.gov/pubmed/35690873 http://dx.doi.org/10.1186/s13326-022-00272-6 |
work_keys_str_mv | AT binkhedersamar phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT wuhengyi phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT quinneysarak phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT zhangshijun phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT zitumdmuntasir phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT chiangchienwei phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT wanglei phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT jonesjosette phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature AT lilang phenodefacorpusforannotatingsentenceswithinformationofphenotypedefinitionsinbiomedicalliterature |