Leveraging Word Embeddings and Semantic Enrichment for Automatic Clinical Evidence Grading
Clinical practice guidelines are supported by the best available evidence from biomedical publications to assist clinical decision making.The recent technological advances in natural language processing and text mining have the potential in reducing the labor cost and time consumption of creating and updating the guidelines, and improving the quality of clinical recommendations.In order to identify high-quality biomedical publications automatically, we proposed an approach to classify unstructured biomedical text documents into predefined clinical evidence levels based on the linguistic features and semantic enrichment.We investigated the feasibility of leveraging word embeddings for clinical evidence grading that is formulated as a text classification problem, and proposed some strategies for semantic enrichment by incorporating the domain knowledge extracted from the knowledge bases and semantic networks.Moreover, we evaluated the proposed method by applying it to the clinical guidelines of breast cancer.The preliminary results demonstrated that the proposed method performed better than the widely-used baseline methods, and appropriate semantic enrichment could further improve the performance for this challenging task.
Text Mining Semantic Enrichment Clinical Evidence Grading
Haolin Wang Yuming Qiu Jun Jiang Ju Zhang Jiahu Yuan
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing, Chin Chengdu Institute of Computer Applications, Chinese Academy of Sciences Chengdu, China;University of Breast and Thyroid Surgery,Southwest Hospital Chongqing, China Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing, Chin
国际会议
成都
英文
133-137
2018-03-12（万方平台首次上网日期，不代表论文的发表时间）