Structured Processing for Pathological Reports Based on Dependency Parsing

Most of pathological reports are unstructured texts which can not be directly analyzed by computers. The current researches on structured texts mainly focus on the information extraction. However, the syntactic features of pathological reports are particular, which makes it more difficult to extract information relations. To solve this problem, a novel method of structuralizing pathological reports based on syntactic and semantic features is proposed in this paper. First of all, we construct a synonym lexicon by using neural network language models to eliminate the phenomenon of synonymy. Then the dependency trees are generated based on the preprocessed pathological reports to extract medical examination indices. Meanwhile, we use short-sentence segmentation and annotation as optimized strategies to simplify the structure of dependency trees, which makes the grammatical relations of medical texts clearer and improves the quality of the structured results. Finally the key-value pairs of medical examination indices can be extracted from pathological reports in Chinese, and the structured texts can be generated automatically. Experimental results based on real pathological report data sets show that the performance of the proposed method on medical indices and values extraction achieves 82.91% and 79.11% of accuracy, which provides a solid foundation for related studies in the future.