Improving Disease Named Entity Recognition for Clinical Trial Matching

Disease named entity recognition (NER) is an important enabling technology to develop various downstream biomedical natural language processing applications. This is a challenging task, which requires addressing potential ambiguities due to variable contextual usage of the disease name mentions in clinical texts. In particular, clinical trial texts have unique complexities compared to patient-focused clinical reports or information-rich biomedical research articles, as they typically define drug testing eligibility requirements for patient cohorts via compound contextual and logical relationships. In this paper, we propose a novel disease NER model for clinical trial texts by using deep contextual embeddings with relevant domain-specific features, word embeddings, and character embeddings in a bidirectional long short-term memory network-conditional random field (BiLSTM-CRF) framework. Experiments and analyses on a clinical trial dataset and the benchmark NCBI scientific article dataset show the effectiveness of the proposed model.