Objective: This research explores the method and application of intelligent extraction and calculation of daily average dosage for Chinese patent medicines in drug price monitoring, to provide key data support for improving the refinement and scientificity of drug price monitoring. Methods: In view of the difficulties of complex dosage forms of Chinese patent medicines, multi-scenario descriptions in package inserts, and the difficulty of traditional manual extraction of daily dosage, an intelligent system was developed on the DeepSeek open-source framework, taking the application of artificial intelligence (AI) technology to extract the daily average dosage of traditional Chinese patent medicines on the Shenzhen Drug Trading Platform as a practical case. This system synergistically employs natural language processing (NLP), rule engine, and dual anomaly detection algorithm. NLP is utilized for the precise extraction of dosage, frequency, and unit information from the package inserts. The rule engine enables multi-scenario segmentation and accurate daily dosage calculation. The dual anomaly detection algorithm provides a reverse validation mechanism to correct data inconsistencies. Results: Practice shows that the intelligent system attained an accuracy rate of 95.51% when processing 12000 Chinese patent medicine usage and dosage records. The F1 value of the dual anomaly detection algorithm reaches 0.9413, indicating that the system has good comprehensive capabilities such as language processing of traditional Chinese patent medicine usage and dosage, and self anomaly detection. Extraction efficiency is 12 times higher than traditional manual processing. The lightweight local deployment solution concurrently ensured data security. Conclusion: This study validates that AI technology provides practical technical solutions for calculating price differences of Chinese patent medicines, identifying covert price manipulation, supporting dynamic drug price monitoring, and compiling price indices of Chinese patent medicines. It represents initial exploratory efforts toward intelligent and standardized development in the pharmaceutical industry.
Key words
artificial intelligence /
Chinese patent medicines /
daily average dosage /
natural language processing /
F1 /
drug price monitoring
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 国家医疗保障局.国家医疗保障局关于印发《关于做好当前药品价格管理工作的意见》的通知(医保发〔2019〕67号)[EB/OL].(2019-11-06)[2025-06-20].https://www.nhsa.gov.cn/art/2019/12/6/art_53_2150.html.
[2] 新华社.中共中央国务院关于深化医疗保障制度改革的意见[EB/OL].(2020-02-25)[2024-12-25].https://www.gov.cn/zhengce/2020-03/05/content_5487407.htm.
[3] 国家医疗保障局.国家医疗保障局办公室关于做好2023年医药集中采购和价格管理工作的通知[EB/OL].(2023-03-01)[2024-12-25].https://www.nhsa.gov.cn/art/2023/3/1/art_109_10207.html.
[4] 新华社.中共中央关于进一步全面深化改革推进中国式现代化的决定[EB/OL].(2024-07-21)[2024-12-25].https://www.gov.cn/zhengce/202407/content_6963770.htm.
[5] 崔磊强,石菊,王小倩,等.药品价格监测与趋势分析——以南宁市为例[J].中国医疗保险,2025(02):59-67.
[6] 国家发展改革委.关于印发《药品差比价规则》的通知(发改价格〔2011〕2452号)[EB/OL].(2011-11-17)[2023-03-15].https://www.gov.cn/gzdt/2011-12/01/content_2008066.htm.
[7] 深圳市全药网药业有限公司.深圳交易平台药品价格指数[EB/OL].(2024-09-30)[2024-12-25].https://gpooss.qywgpo.com/webstatic/%E4%BB%B7%E6%A0%BC%E6%8C%87%E6%95%B0web.html#page_8.
[8] 马芳芳,吴晶.药品价格指数的方法学综述[J].中国卫生政策研究,2015,8(7): 61-67.
[9] Zaifu Zhan,Shuang Zhou,Huixue Zhou,et al. An evaluation of DeepSeek Models in Biomedical Natural Language Processing.[J].arXiv preprint arXiv:2503.00624,2025. https://arxiv.org/abs/2503.00624.
[10] Patel J, Reiner J, Stilwell B, et al. Leveraging K-Means Clustering and Z-Score for Anomaly Detection in Bitcoin Transactions[J].Informatics.2025; 12(2):43. https://doi.org/10.3390/informatics12020043
[11] Peter J. Rousseeuw, Mia Hubert. Anomaly detection by robust statistics[J].WIREs Data Mining and Knowledge Discovery2018, 8:e1236. https://doi.org/10.1002/widm.1236
[12] Cai D., Shi S., Jiang S.et al. Estimation of the cost-effective threshold of a quality-adjusted life year in China based on the value of statistical life[J].Eur J Health Econ 23,607-615(2022). https://doi.org/10.1007/s10198-021-01384-z.
