CMedCausal - A dataset of Chinese medical causal relationship extraction

Corresponding author: Chen Mosha, chenmosha.cms@alibaba-inc.com

DOI: 10.12201/bmr.202211.00004

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  Abstract: Modern medicine emphasizes interpretability and requires doctors to give reasonable, well-founded and con- vincing diagnostic results when diagnosing patients. Therefore, there are a large number of causal correlations in medical concepts such as symptoms, diagnosis and treatment in the text of the results of the inquiry. Explanation of relationships, and mining these relationships from text is of great help in improving the accuracy and inter- pretability of medical searches. Based on this, this paper constructs a new medical causality extraction dataset CMedCausal (Chinese Medical Causal dataset), which defines three key types of medical causal explanation and reasoning relationships: causal relationship, conditional relationship, and hypothetical relationship. It consists of 9,153 medical texts with a total of 79,244 entity relationships annotated. Researchers can carry out research on medical causal relationship mining and medical causal interpretation map construction based on CMedCausal. At the same time, relying on the 8th China Conference on Health Information Processing (CHIP2022), we also held the evaluation task of ”Medical Causal Entity Relationship Extraction”, aiming to promote the development of Chinese medical causal relationship mining technology.

  Key words: causal relationship, relation extraction, interpretability

  Submit time: 14 November 2022

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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