Research and Design of Intelligent Text Retrieval Technology for Semantic Electronic Medical Records

Corresponding author: SHEN Yanni, 17791972986@163.com

DOI: 10.12201/bmr.202512.00082

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  Abstract: Objective/Significance Traditional retrieval methods perform poorly when faced with the semantic complexity, context dependency, and unstructured data unique to the medical field, failing to meet the urgent demand for efficient and intelligent information retrieval in precision medicine and smart medical research. To address this, the study aims to establish a semantic-oriented intelligent retrieval method to achieve semantic interoperability in electronic medical record (EMR) text data and enhance retrieval efficiency. Method/Process The study combines enhanced traditional keyword retrieval with deep learning-based high-order semantic matching retrieval. First, by introducing a medical terminology database into the traditional keyword retrieval function, a hierarchical retrieval extension mechanism is established to improve retrieval performance. Second, a semantic vector representation model is developed based on semantic similarity strategies, extracting EMR semantic vectors to form a semantic retrieval database, which generates recommended retrieval results through semantic comparison. Finally, the keyword expansion retrieval and semantic comparison results are combined to produce the final retrieval conclusions. Result/Conclusion Testing demonstrates that the semantic-oriented intelligent EMR retrieval method enables multi-level, multi-angle text retrieval functionality. Compared to traditional keyword and knowledge graph retrieval methods, it significantly improves text semantic retrieval accuracy (p<0.05) and recall rate (p<0.05), providing a feasible solution for efficient and precise EMR intelligent retrieval.

  Key words: Electronic medical records; Intelligent retrieval; Deep learning; Semantic analysis; Semantic retrieval

  Submit time: 31 December 2025

  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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  1	2025-10-09	10.12201/bmr.202512.00082V1	Download

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