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Conditional Relationship Extraction for Diseases and Symptoms by a Web Search-Based Approach

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This paper studies the strategies of automatically extracting the conditional relationships between diseases and symptoms from a Chinese encyclopedia site and the disease-related web pages searched from the Internet. At first, the seed symptoms of a disease are extracted from an online medical encyclopedia automatically. These seed symptoms are utilized as query keywords to automatically find more symptoms of a disease from the unstructured documents of the disease-related search results. Next, a jointly learning method is used to construct the embedded representations of the conditional terms and pattern terms. Besides, the semantic similarity matrix of conditional terms is computed through the co-clustering algorithm to discover the representative conditional terms of the clusters. The result of experiments shows that the proposed method, which discovers the semantically related symptoms of a disease associated with conditionals, achieves high accuracy. Besides, many unusually known symptoms considered by the medical experts are discovered, which may be noticeable symptoms needing further verification in the future.




Lee, Y. H., & Koh, J. L. (2019). Conditional Relationship Extraction for Diseases and Symptoms by a Web Search-Based Approach. In Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018 (pp. 554–561). Institute of Electrical and Electronics Engineers Inc.

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