Research on automatic recommendation method for answer analysis of pediatric medical examination questions for knowledge question and answerWang Juan1, Hou Li1, Sun Yue-ping1, Li Jia-ming1,Dong Liang-guang2 Li Yun-han3

Corresponding author: HouLi, hou.li@imicams.ac.cn

DOI: 10.12201/bmr.202409.00026

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  Abstract: Purpose/Significance To explore and implement the automated interpretation of pediatric medical exam answers, aimed at enhancing the efficiency and quality of answer explanation compilation. Method/Process This paper proposes a method that combines Latent Semantic Indexing, BERT, and the CoSENT model. Initially, multiple candidate answer explanations are extracted from reference documents using the Latent Semantic Indexing method and the MC-BERT model. Subsequently, the CoSENT model is employed to calculate the similarity between the candidate explanations and the question stems as well as the answer options. The candidate explanation with the highest similarity is then selected as the final answer explanation. Result/Conclusion The experimental results show that the method presented in this paper achieved a precision rate of 72.6%, effectively enhancing the efficiency of compiling question answer explanations, reducing the burden on educators, and providing significant data support for educational research.

  Key words: medical test questions; reference analysis; latent semantic indexing; BERT; CoSENT

  Submit time: 14 September 2024

  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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