Relation Extraction between Medical Entities using Deep Learning Approach - Patel, Ruchi; Tanwani, Sanjay; Patidar, Chhaya (Informatica, 2021)
Medical discharge summaries or patient prescriptions contain variety of medical terms. The semantic relation extraction between medical terms is essential for 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, 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 train data. The performance evaluation of relation extraction task is done using precision and recall. The system achieved average 75% precision and 72% recall. The performance of the system is compared with awarded i2b2 participated systems. Keywords: Convolution Neural Network;Feature Extraction;Relation Classification;Word Embedding.