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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Research on structured parsing of medical consultation system text based on prior knowledge

Liu Yao1
Zhai Yu2
Zhou Jiahui1
1. Information Technology Support Center, Institute of Scientific & Technical Information of China, Beijing 100038, China
2. Dept. of Chinese & Bilingual Study, The Hong Kong Polytechnic University Hong Kong 999077, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0263

Abstract

Structured parsing of domain texts such as medical books and patient-related texts such as patients' statements can provide structured resource support for the construction of medical consultation system, which can help assist doctors in diagnosis. Therefore, this paper proposed a method for structured parsing. The method used engineering approaches to obtain domain-specific prior knowledge and training data. Based on the BERT model, the method integrated medical prior knowledge into the model through masked language model and entity ranking tasks to obtain the MedReBERT model enhanced by the prior knowledge. The method designed the text generation template to train concept indexing and concept relationship indexing models, to realise the indexing of concepts and relationships. This paper proposed a collaborative framework that combined engineering processes with algorithmic learning. The experimental results show that the method can realize the structured parsing of the text. The proposed framework can establish interfaces between algorithms and realize the low-threshold iterative application of algorithms related to the structured parsing of text in the consultation system.

Foundation Support

国家社会科学基金资助项目(21BTQ011)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0263
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-11] Accepted Paper

Cite This Article

刘耀, 翟雨, 周家辉. 基于先验知识的问诊系统文本结构化解析方法 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0263. (Liu Yao, Zhai Yu, Zhou Jiahui. Research on structured parsing of medical consultation system text based on prior knowledge [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0263. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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