Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation

• Main Authors: Yiqing Zhao, Nooshin J. Fesharaki, Hongfang Liu, Jake Luo
• Format: Article
• Language: English
• Published: BMC 2018-07-01
• Series: BMC Medical Informatics and Decision Making
• Subjects: Knowledge modeling; Sublanguage analysis; Natural language processing; Semantic network; Big data analysis; Medical imaging
• Online Access: http://link.springer.com/article/10.1186/s12911-018-0645-3

Abstract Background The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline. Methods As a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org , to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the “Subject:Relationship:Object” syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources. Results A semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%. Conclusion The results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain.