Annotating and detecting phenotypic information for chronic obstructive pulmonary disease

Author:

Ju Meizhi1,Short Andrea D2,Thompson Paul1,Bakerly Nawar Diar3,Gkoutos Georgios V45678,Tsaprouni Loukia9,Ananiadou Sophia1

Affiliation:

1. National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, UK

2. Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK

3. Salford Royal NHS Foundation Trust; and School of Health Sciences, The University of Manchester, Manchester, UK

4. College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, UK

5. Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

6. MRC Health Data Research UK (HDR UK)

7. NIHR Experimental Cancer Medicine Centre, Birmingham, UK

8. NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, UK

9. School of Health Sciences, Centre for Life and Sport Sciences, Birmingham City University, Birmingham, UK

Abstract

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.

Funder

Manchester Molecular Pathology Innovation Centre

National Science Foundation

NIHR

NIHR Birmingham Biomedical Research Centre

National Institute for Health Research

Medical Research Council

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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