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

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Abstract

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.

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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. https://doi.org/10.1109/WI.2018.00-38

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