[1]孙渤星,周 骥,刘湘宁,等.基于数据挖掘技术提升中药院内制剂研发潜力的探索[J].医学信息,2025,38(16):1-5,12.[doi:10.3969/j.issn.1006-1959.2025.16.001]
　SUN Boxing,ZHOU Ji,LIU Xiangning,et al.Exploration on Improving the Research and Development Potential of In-Hospital Preparations of Traditional Chinese Medicine Based on Data Mining Technology[J].Journal of Medical Information,2025,38(16):1-5,12.[doi:10.3969/j.issn.1006-1959.2025.16.001]
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基于数据挖掘技术提升中药院内制剂研发潜力的探索()

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
38卷
期数:
2025年16期
页码:
1-5,12
栏目:
中医药信息学
出版日期:
2025-08-15

文章信息/Info

Title:
Exploration on Improving the Research and Development Potential of In-Hospital Preparations of Traditional Chinese Medicine Based on Data Mining Technology
文章编号:
1006-1959（2025）16-0001-06
作者:
孙渤星; 周 骥; 刘湘宁; 樊 慧; 伏安成; 鲜颖松; 冯成云; 唐 艳
广元市中医医院运营管理部，四川 广元 628000
Author(s):
SUN Boxing;  ZHOU Ji;  LIU Xiangning;  FAN Hui;  FU Ancheng;  XIAN Yingsong;  FENG Chengyun;  TANG Yan
Department of Operation Management, Guangyuan Hospital of Traditional Chinese Medicine, Guangyuan 628000, Sichuan, China
关键词:
中药院内制剂; 数据挖掘; 关联规则; 综合评价
Keywords:
In-hospital traditional Chinese medicine preparation;  Data mining;  Association analysis;  Comprehensive evaluation
分类号:
R283
DOI:
10.3969/j.issn.1006-1959.2025.16.001
文献标志码:
A
摘要:
目的 对名老中医用药经验进行快速分析提炼，发掘中药院内制剂研发潜力，探索提升中药院内制剂研发效率的策略。方法 采用大数据技术对医院名老中医门诊诊疗数据进行汇总整理，通过Aprior关联规则分析法结合Topisi综合评价法，快速识别具有较高潜力开发中药院内制剂的用药经验。结果 2位专家共7389条诊疗数据，标准化处理筛选出6268条数据进行关联规则分析，涉及60个中医病种、438种中药。用药规律量化评估排序结果显示，专家1诊疗的夜啼病、喉痹病、小儿口疮病，以及专家2诊疗的乳癖病、热淋病、鼻渊病，共6个病种的用药规则在支持度、置信度及综合评价中表现最优，具有较高潜力研发为中药院内制剂；在前20位高潜力研发病种中，专家1占9个（占其总病种的64.29%）。结论 通过大数据技术能大幅度缩短名老中医经验方的总结周期，在提高临床诊疗数据质量的前提下，能有效提高中药院内制剂研发效率；组建复合型研发团队，将有效促进诊疗数据资源的开发与利用。
Abstract:
Objective To rapidly analyze and refine the medication experience of famous veteran doctors of traditional Chinese medicine, explore the potential of research and development of in-hospital preparations of traditional Chinese medicine, and explore strategies to improve the efficiency of research and development of hospital preparations of traditional Chinese medicine. Methods Big data technology was used to summarize and sort out the outpatient diagnosis and treatment data of famous veteran doctors of traditional Chinese medicine in the hospital. Aprior association rule analysis method combined with Topisi comprehensive evaluation method was used to quickly identify the medication experience with high potential for developing in-hospital preparations of traditional Chinese medicine. Results A total of 7389 diagnosis and treatment data from 2 experts were standardized and screened, and 6268 data were selected for association rule analysis, involving 60 traditional Chinese medicine disease types and 438 kinds of Chinese medicinal materials. By quantifying and ranking the medication rules of the included diseases through association rule analysis, it was found that the medication rules for 6 diseases (nocturnal crying, laryngopharyngitis, infantile oral ulcer treated by Expert 1; breast hyperplasia, heat strangury, sinusitis treated by Expert 2) showed the best performance in terms of support, confidence and comprehensive evaluation, with high potential to be developed into in-hospital traditional Chinese medicine preparations. Among the top 20 diseases with high R&D potential, 9 were treated by Expert 1 (accounting for 64.29% of his total diseases).Conclusion Data mining technology can significantly shorten the summary cycle of the experience prescriptions of famous veteran doctors of traditional Chinese medicine. On the premise of improving the quality of clinical diagnosis and treatment data, it can effectively enhance the R&D efficiency of in-hospital traditional Chinese medicine preparations; establishing an interdisciplinary R&D team will effectively promote the development and utilization of diagnosis and treatment data resources.

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更新日期/Last Update: 1900-01-01