A NLP System of DCUMT in NTCIR-11 MedNLP-2: RNN for ICD/Time Entity Recognition and ICD Classification Tasks

This paper describes the medical NLP system developed at Dublin City University for participation in the Second Medical NLP Shared Task (MedNLP 2) in NTCIR-11 [1]. This shared task is a Japanese task. Our system detects International Classification of Diseases (ICD) and time entities and classifies ICD entities. We participated in the task 1 which detects the ICD and time entities, and the task 2 which classifies the detected ICD entities among the ICD codes. Our system uses deep learning to learn and classify those entities. Our result was F1 score of 67.8 for the ICD entity recognition task (task 1), 77.4 for the time entity recognition task (task 1), and 54.0 for the ICD classification task (task 2 for gold standard).