Objective Chronic obstructive pulmonary disease (COPD) phenotypes cover a range of lung abnormalities. To allow text mining (TM) 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 recogniser 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 (BiLSTM)-CRF network firstly recognises nested mentions, which are fed into subsequent BiLSTM-CRF layers, to help to recognise 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 recognising detailed phenotypic information.
Discussion Information extracted by our method can facilitate efficient location and exploration of detailed information about phenotypes, e.g., 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.