Research on Chinese electronic medical record entity mapping method by fusing similarity algorithm and pre-trained model

Corresponding author: renhuiling, ren.huiling@imicams.ac.cn

DOI: 10.12201/bmr.202305.00015

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  Abstract: Purpose/SignificanceIn order to fully explore and utilize the physical resources of Chinese electronic medical records, the combination of algorithms and models suitable for large-scale entity mapping without manually constructing regular features is studied. Method/Process The self-annotated Chinese electronic medical record standard dataset is used to fuse the similarity algorithm and the pre-trained model, and the candidate entity generation and entity disambiguation stages of entity mapping are applied respectively, and the performance of different similarity algorithms and pre-trained models selected in this paper is compared and analyzed in this process. Results/Conclusion A method to improve the effect of drug-like entity mapping is proposed, and the combination of Jaccard similarity algorithm and Bert pre-training model is finally determined, which achieves more than 90% accuracy and 99% recall in the entity mapping task, which can efficiently realize the entity mapping task of massive Chinese electronic medical records.

  Key words: Entity mapping; Entity standardization; Similarity algorithm; Electronic medical records

  Submit time: 17 May 2023

  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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• ID	Submit time	Number	Download
  1	2023-02-20	bmr.202305.00015V1	Download

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