图书情报工作

图书情报工作 >

2024 , Vol. 68 >Issue 19: 40 - 53

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.19.004

情报研究

中医药领域科技文献临床应用价值评价方法研究

  • 范萌 ,
  • 常志军
展开
  • 1 中国科学院文献情报中心 北京 100190;
    2 中国科学院大学经济与管理学院信息资源管理系 北京 100190
范萌,硕士研究生,提出研究思路,设计研究框架,撰写与修改论文

收稿日期: 2024-03-06

  修回日期: 2024-06-17

  网络出版日期: 2024-10-17

基金资助

本文系国家社会科学基金项目“面向循证医学的领域文献实体关系识别方法研究”（项目编号：21BTQ106）研究成果之一。

收起

Research on the Evaluation Method of Clinical Application Value of Scientific and Technological Literature in the Field of Traditional Chinese Medicine

  • Fan Meng ,
  • Chang Zhijun
Expand
  • 1 Documentation and Information Center, National Science Library, Chinese Academy of Sciences, Beijing 100190;
    2 Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190

Received date: 2024-03-06

  Revised date: 2024-06-17

  Online published: 2024-10-17

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on entity relationship recognition method of domain literature for evidence-based medicine” (Grant No. 21BTQ106)).

Fold

摘要

[目的/意义] 为解决当前我国中医药领域科技文献在临床应用价值评价方面标准不统一、评价指标不完善的问题，构建一套临床应用价值评价体系，并设计中医药领域科技文献的评价系统，以帮助科研人员筛选高质量文献，推动临床研究的进展。[方法/过程] 基于文献规范性、计量学特征、中医药领域特点和临床实践指南，构建评价指标体系。通过德尔菲法和层次分析法对指标进行优化并确定权重。结合规则与深度学习方法识别指标，通过加权求和获得文献临床应用价值分值。最后，选取10篇相关文献测试系统有效性和可行性。[结果/结论] 实验结果表明，系统输出的得分能够较好地反映文献的临床应用价值，该评价系统具有科学性和有效性。

关键词： 中医药; 文献质量; 评价体系; 信息抽取; 文本分类

本文引用格式

范萌 , 常志军 . 中医药领域科技文献临床应用价值评价方法研究[J]. 图书情报工作, 2024 , 68(19) : 40 -53 . DOI: 10.13266/j.issn.0252-3116.2024.19.004

Abstract

[Purpose/Significance] This study aims to solve the problems of inconsistent standards and incomplete evaluation indicators of scientific and technological documents in the current Traditional Chinese medicine field in China. This article has established a clinical application value evaluation system, and has designed an evaluation system for scientific and technological documents in the field of Chinese medicine to help researchers screen high-quality literature and promote the progress of clinical research. [Method/Process] Based on the standardization of literature, the characteristics of measurement, the characteristics of traditional Chinese medicine, and the clinical practice guide, the evaluation indicator system was established. The Delphi method and Analytic Hierarchy Process (AHP) were employed to optimize the indicators and determine their respective weights. Indicators were identified by combining rule-based methods and deep learning. The value score of the clinical literature was obtained through weighted summation. Finally, 10 relevant articles were selected to test the effectiveness and feasibility of the system. [Result/Conclusion] The experimental results show that the scores generated by the system can reflect the clinical application value of the literature, the evaluation system is scientific and effective.

Key words： traditional Chinese medicine; literature quality; evaluation system; information extraction; text classification

参考文献

[1] 李敬华, 王家良, 崔蒙. 中医临床文献质量评价研究现状及方法分析[J]. 中国中医药信息杂志, 2008(6): 95-98. (LI J H, WANG J L, CUI M. Current status and method analysis of quality evaluation of traditional Chinese medicine clinical literature[J]. Chinese journal of information on traditional Chinese medicine, 2008(6): 95-98.)
[2] 国务院. 国务院关于印发中医药发展战略规划纲要（2016—2030年）的通知[J]. 中华人民共和国国务院公报, 2016(8): 21-29. (State Council. Notice of the State Council on the outline of the strategic planning of traditional Chinese medicine development (2016-2030) [J]. Gazette of the State Council of the People's Republic of China, 2016(8): 21-29.)
[3] 国务院办公厅. 国务院办公厅关于完善科技成果评价机制的指导意见[J]. 中华人民共和国国务院公报, 2021(23): 22-25. (Office of the State Council. The General Office of the State Council on improving the evaluation mechanism of scientific and technological achievements[J]. Gazette of the State Council of the People's Republic of China, 2021(23): 22-25.)
[4] MOHER D, JONES A, LEPAGE L, et al. Use of the CONSORT statement and quality of reports of randomized trials[J]. JAMA, 2001, 285(15): 1992-1995.
[5] JADAD AR, MOORE RA, CARROLL D, et al. Assessing the quality of reports of randomized clinical trails: is blinding necessary[J]. Control clin trails, 1996, 17(1): 1-12.
[6] HIGGINS J P, GREEN S. Cochrane handbook for systematic reviews of interventions[M]. John Wiley, 2008.
[7] 许巍, 熊俊, 陈日新, 等. 针灸治疗帕金森病随机对照研究质量评价[J]. 中华中医药学刊, 2017, 35(3): 562-565. (XU W, XIONG J, CHEN R X, et al. Quality evaluation of randomized controlled trails on acupuncture and moxibustion treatment of Parkinson’s disease[J]. Chinese archives of traditional Chinese medicine, 2017, 35(3): 562-565.)
[8] 季雯. 近十年中医药为主治疗血尿的临床文献质量评价[D]. 沈阳: 辽宁中医药大学, 2010. (JI W. Quality evaluation of clinical literature on hematuria treated mainly by traditional Chinese medicine in the last decade[D]. Shenyang: Liaoning University of Traditional Chinese Medicine, 2010.)
[9] 王瑞平, 李斌. 随机对照临床试验CONSORT声明解读[J]. 上海医药, 2022, 43(5): 58-62. (WANG R P, LI B. Interpretation of CONSORT statements on randomized controlled clinical trail[J]. Shanghai medical & pharmaceutical journal, 2022, 43(5): 58-62.)
[10] 王辉, 黄晓林, 蒋欣宏. 科技报告文献质量评价体系构建及实证研究[J]. 湘潭大学学报(哲学社会科学版), 2021, 45(5): 188-193. (WANG H, HUANG X L, JIANG H X. Construction of and empirical study on the quality evaluation system of scientific and technical report literature[J]. Journal of Xiangtan University (philosophy and social sciences edition), 2021, 45(5): 188-193.)
[11] 胡可慧. 我国医疗人工智能相关政策实施效果的评价指标体系研究[D]. 北京: 北京中医药大学, 2020. (HU K H. Research on the evaluation indicator system for the implementation effectiveness of medical artificial intelligence related policies in China[D]. Beijing: Beijing University of Chinese Medicine, 2020.)
[12] LAFFERTY J D, MCCALLUM A K, PEREIRA F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//International conference on machine learning. Morgan Kaufmann Publishers, 2001.
[13] HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural computation, 1997, 9(8): 1735-1780.
[14] CUN Y L, BOTTOU L, BENGIO Y. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[15] BROWN T, MANN B, RYDER N, et al. Language models are few-shot leaners[C]//Proceedings of the neural information processing systems, 2020: 1877-1901.
[16] LU Y, BU L, CHEN L, et al. Extracting clinical experiences from ancient literature of traditional Chinese medicine via deep learning[J]. Journal of Sichuan University, 2022, 59(2): 109-116.
[17] PANG Y, QIN X, ZHANG Z. Specific relation attention-guided graph neural networks for joint entity and relation extraction in Chinese EMR[J]. Applied sciences-basel, 2022, 12(17): 8493.
[18] LI C, XIE D. Research on the automatic extraction method of admission record information from traditional Chinese medicine of electronic medical records[J]. Modernization of traditional Chinese medicine and materia medica-world science and technology, 2023, 25(5): 1615-1622.
[19] QIN X, XIONG J, WANG Y, et al. Integrating syndrome factor analysis and “Prescription Name-Medicine Name” similarity to mine primary medicines in traditional Chinese medicine[J]. Journal of Sichuan University, 2011, 48(1): 67-72.
[20] XIE J, FANG P, HU K, et al. Research on data extraction and cleaning methods of clinical diagnosis and treatment of modern famous veteran doctors of TCM[J]. Lishizhen medicine and materia medica research, 2017, 28(11): 2786-2788.
[21] RUAN C, WU Y, LUO G S, et al. Relation extraction for Chinese clinical records using multi-view graph learning[J]. IEEE access, 2020, 8: 215613-215622.
[22] WANG X, YANG T, GAO X, et al. Knowledge graph enhanced transformers for diagnosis generation of Chinese medicine[J]. Chinese journal of integrative medicine, 2024, 30: 267-276.
[23] HU H, CHENG C, YE Q, et al. Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification[J]. Mathematical biosciences and engineering, 2024, 21(1): 369-391.
[24] SUN Y, ZHAO Z, WANG Z, et al. Leveraging a joint learning model to extract mixture symptom mentions from traditional Chinese medicine clinical notes[J]. Biomed research international, 2022, 2022.
[25] XIA Y, CAI J, LI Y, et al. A precision-preferred comprehensive information extraction system for clinical articles in traditional Chinese medicine[J]. International journal of intelligent systems, 2022, 37(8): 4994-5010.
[26] BAI T, GUAN H, WANG S, et al. Traditional Chinese medicine entity relation extraction based on CNN with segment attention[J]. Neural computing & applications, 2022, 34(4, SI): 2739-2748.
[27] JIN Q, ZHAO X, YANG H, et al. Image feature extraction and retrieval of the Euler number to Chinese herbal medicine based on PCNN[C]//20193RD international conference on computer graphics digital image processing. 2019, 1355(1): 12-16.
[28] MIAO J, HUANG Y, WANG Z, et al. Image recognition of traditional Chinese medicine based on deep learning[J]. Frontiers in bioengineering and biotechnology, 2023, 11: 239.
[29] BREIMAN L, FRIEDMAN J, OLSHEN R A, et al. Classification and regression trees[M]. Belmont: Wadsworth, 1984.
[30] MARON M E, KUHNS J L. On relevance probabilistic indexing and information retrieval[J]. Journal of the ACM, 1960, 7(3): 216-244.
[31] COVER T M, HART P E. Nearest neighbor pattern classification[J]. IEEE transactions on information theory, 1967, 13(1): 21-27.
[32] LAN G, HU M, LI Y, et al. Contrastive knowledge integrated graph neural networks for Chinese medical text classification[J]. Engineering applications of artificial intelligence, 2023: 122.
[33] LIANG S, SUN F, SUN H, et al. A medical text classification approach with ZEN and capsule network[J]. Journal of super computing, 2023: 4353-4377.
[34] CHEN X, LONG C, NIU Z, et al. Classification research of Chinese medicine based on latent semantic analysis and NIR[J]. Acta optica sinica, 2014, 34(9): 0930001-1-0930001-6.
[35] GU T, YAN Z, JIANG J. Classifying Chinese medicine constitution using multimodal deep-learning model[J]. Chinese journal of integrative medicine, 2024, 30(2): 163-170.
[36] LIU Z, PENG E, YAN S, et al. T-know: a knowledge graph-based question answering and information retrieval system for traditional Chinese medicine[C]//International conference on computational linguistics. 2018: 15-19.
[37] GAO R, LI C. Knowledge question-answering system based on knowledge graph of traditional Chinese medicine[C]//2020 IEEE 9th joint international information technology and artificial intelligence conference. 2020.
[38] ZOU Y, HE Y, LIU Y. Research and implementation of intelligent question answering system based on knowledge graph of traditional Chinese medicine[C]//202039th Chinese control conference. Shenyang. 2020: 4266-4272.
[39] 杨小波, 梁兆晖, 罗云坚, 等. 支持向量机算法在中医证候信息分类中的应用[J]. 世界科学技术-中医药现代化, 2007(1): 28-31. (YANG X B, LIANG Z H, LUO Y J, et al. P-SVM applications in TCM syndrome classifications[J]. Modernization of traditional Chinese medicine and materia medica-world science and technology, 2007(1): 28-31.)
[40] 邢雁辉, 崔蒙, 储戟农, 等. 基于贝叶斯分类算法的治疗中风中药组方研究[J]. 中西医结合心脑血管病杂志, 2015, 13(4): 471-474. (XING Y H, CUI M, CHU J N, et al. Analyzing the data of Chinese medicine used in disease treatment of stroke based on the Bayes analysis[J]. Chinese journal of integrative medicine on cardio-cerebrovascular disease, 2015, 13(4): 471-474.)
[41] MINTZ M, BILLS S, SNOW R, et al. Distant supervision for relation extraction without labeled data[C]//Proceedings of the 47th annual meeting of the Association for Computational Linguistics and the 4th international joint conference on natural language processing of the AFNLP. Singapore: Association for Computational Linguistics, 2009.
[42] NGUYEN T V T, MOSCHITTI A. End-to-end relation extraction using distant supervision from external semantic repositories[C]// Association for Computational Linguistics. 2011.
Options
文章导航

/

〈 〉