Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes

Anika Oellrich; Nigel Collier; Damian Smedley; Tudor Groza

doi:10.1371/journal.pone.0116040

Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes

PLoS One. 2015 Jan 21;10(1):e0116040. doi: 10.1371/journal.pone.0116040. eCollection 2015.

Authors

Anika Oellrich¹, Nigel Collier², Damian Smedley¹, Tudor Groza³

Affiliations

¹ Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom.
² EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, United Kingdom; National Institute of Informatics, Tokyo 101-8430, Japan.
³ School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia; Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia.

Abstract

Electronic health records and scientific articles possess differing linguistic characteristics that may impact the performance of natural language processing tools developed for one or the other. In this paper, we investigate the performance of four extant concept recognition tools: the clinical Text Analysis and Knowledge Extraction System (cTAKES), the National Center for Biomedical Ontology (NCBO) Annotator, the Biomedical Concept Annotation System (BeCAS) and MetaMap. Each of the four concept recognition systems is applied to four different corpora: the i2b2 corpus of clinical documents, a PubMed corpus of Medline abstracts, a clinical trails corpus and the ShARe/CLEF corpus. In addition, we assess the individual system performances with respect to one gold standard annotation set, available for the ShARe/CLEF corpus. Furthermore, we built a silver standard annotation set from the individual systems' output and assess the quality as well as the contribution of individual systems to the quality of the silver standard. Our results demonstrate that mainly the NCBO annotator and cTAKES contribute to the silver standard corpora (F1-measures in the range of 21% to 74%) and their quality (best F1-measure of 33%), independent from the type of text investigated. While BeCAS and MetaMap can contribute to the precision of silver standard annotations (precision of up to 42%), the F1-measure drops when combined with NCBO Annotator and cTAKES due to a low recall. In conclusion, the performances of individual systems need to be improved independently from the text types, and the leveraging strategies to best take advantage of individual systems' annotations need to be revised. The textual content of the PubMed corpus, accession numbers for the clinical trials corpus, and assigned annotations of the four concept recognition systems as well as the generated silver standard annotation sets are available from http://purl.org/phenotype/resources. The textual content of the ShARe/CLEF (https://sites.google.com/site/shareclefehealth/data) and i2b2 (https://i2b2.org/NLP/DataSets/) corpora needs to be requested with the individual corpus providers.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Biological Ontologies
Data Mining / standards*
Electronic Health Records
Humans
Natural Language Processing
Phenotype*
PubMed / standards

Abstract

Publication types

MeSH terms

Grants and funding