Automatic extraction and visualization of semantic relations between medical entities from medicine instructions

Recent years have witnessed the rapid development and tremendous research interests in healthcare domain. The health and medical knowledge can be acquired from many sources, such as professional health providers, health community generated data and textual descriptions of medicines. This paper explores the classification and extraction of semantic relation between medical entities from the unstructured medicine Chinese instructions. In this paper, three kinds of textual features are extracted from medicine instruction according to the nature of natural language texts. And then, a support vector machine based classification model is proposed to categorize the semantic relations between medical entities into the corresponding semantic relation types. Finally, the extraction algorithm is utilized to obtain the semantic relation triples. This paper also visualizes the semantic relations between medical entities with relationship graph for their future processing. The experimental results show that the approach proposed in this paper is effective and efficient in the classification and extraction of semantic relations between medical entities.