Abstract
The Electronic Medical Record (EMR) contains a great deal of medical knowledge related to patients, which has been widely used in the construction of medical knowledge graphs. Previous studies mainly focus on the features based on surface semantics of EMRs for relation extraction, such as contextual feature, but the features of sentence structure in Chinese EMRs have been neglected. In this paper, a fusion dependency parsing-based relation extraction method is proposed. Specifically, this paper extends basic features with medical record feature and indicator feature that are applicable to Chinese EMRs. Furthermore, dependency syntactic features are introduced to analyse the dependency structure of sentences. Finally, the F1 value of relation extraction based on extended features is 4.87% higher than that of relation extraction based on basic features. And compared with the former, the F1 value of relation extraction based on fusion dependency parsing is increased by 4.39%. The results of experiments performed on a Chinese EMR data set show that the extended features and dependency parsing all contribute to the relation extraction.
Highlights
Electronic Medical Record (EMR) contains a vast of medical entities that provide rich medical knowledge
The machine learning method is widely used in the field of medical texts [1,2,3,4], including the task of relation extraction of English EMRs [5], and most of the feature selections rely on English medical dictionaries and data sets [6] as well as syntactic analysis [7]
In order to achieve the task of extracting entity relation of Chinese EMRs more accurately, after analysing the texts of Chinese EMR, this paper extends the features of EMRs based on the basic features that are named extended features, which are mainly divided into medical record features, indicator features, and extended context features
Summary
Electronic Medical Record (EMR) contains a vast of medical entities that provide rich medical knowledge. (4) Relative position: the relative position of two entities, E1 and E2, has a certain indicative function for entity relation extraction in a sentence of Chinese EMR texts.
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