New article describing annotated corpus and detection methods for phenotypic information about COPD


We are pleased to announce the publication of a new article in JAMIA Open describing the construction of a new annotated corpus for phenotypic information about COPD, and novel methods for detecting this information automatically.

Meizhi Ju, Andrea D. Short, Paul Thompson, Nawar Diar Bakerly, Georgios Gkoutos, Loukia Tsaprouni and Sophia Ananiadou (2019) Annotating and detecting phenotypic information for chronic obstructive pulmonary disease, JAMIA Open, ooz009,



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.


Our corpus of 30 full papers (available at: 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.


Information extracted by our method can facilitate efficient location and exploration of detailed information about phenotypes, for example, those specifically concerning reactions to treatments.


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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