Relation Extraction between Medical Entities using Deep Learning Approach

摘要

Medical discharge summaries or patient prescriptions contain a variety of medical terms. The semantic relation extraction between medical terms is essential for the discovery of significant medical knowledge. The relation classification is one of the imperative tasks of biomedical information extraction. The automatic identification of relations between medical diseases, tests, and treatments can improve the quality of patient care. This paper presents the deep learning based proposed system for relation extraction between medical entities. In this paper, a convolution neural network is used for relation classification. The system is divided into four modules: word embedding, feature extraction, convolution, and softmax classifier. The output contains classified relations between medical entities. In this work, data set provided by I2b2 2010 challenge is used for relation detection which consisted of total 9070 relations in test data and 5262 total relations in the train dataset. The performance evaluation of relation extraction task is done using precision and recall. The system achieved an average of 75% precision and 72% recall. The performance of the system is compared with the awarded i2b2 participated systems.