Development of large-scale TCM corpus using hybrid named entity recognition methods for clinical phenotype detection: An initial study

Clinical data is one of the core data repositories in traditional Chinese medicine (TCM) because TCM is a clinically based medicine. However, most clinical data like electronic medical record in TCM is still in free text. Due to the lack of large-scale annotation corpus in TCM field, in this paper, we aim to develop an annotation system for TCM clinical text corpus. To reduce the manual labors, we implement three named entity recognition methods like supervised machine learning method, unsupervised method and structured data comparison, to assist the batch annotations of clinical records before manual checking. We developed the system using Java and have curated more than 2,000 records of chief complaint in an effective way.