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Study on Effective Prescription Recommendation Method of Traditional Chinese Medicine for Type 2 Diabetes Based on Improved Genetic Algorithm
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Hongping LIU1, 2, Jie YANG1, 2, Guoming PANG3, Penghui LI3, Ying XING1, 2, Min WU1, 2, Xiaoxiao WEN1, 2, Hongjiao LI4, Tiancai WEN1, 2
Chinese Archives of Traditional Chinese Medicine | 2025, 43(1) : 39 - 43
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Chinese Archives of Traditional Chinese Medicine | 2025, 43(1): 39-43
• Digital Traditional Chinese Medicine •
Study on Effective Prescription Recommendation Method of Traditional Chinese Medicine for Type 2 Diabetes Based on Improved Genetic Algorithm
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Hongping LIU1, 2, Jie YANG1, 2, Guoming PANG3, Penghui LI3, Ying XING1, 2, Min WU1, 2, Xiaoxiao WEN1, 2, Hongjiao LI4, Tiancai WEN1, 2
Affiliations
  • 1.Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
  • 2.Traditional Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
  • 3.Kaifeng Traditional Chinese Medicine Hospital, Kaifeng 475000, Henan, China
  • 4.Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
Published: 2025-01-10 doi: 10.13193/j.issn.1673-7717.2025.01.007
Outline
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Objective

To improve the genetic algorithm and innovate the recommendation model，mine the core prescriptions and recommend the effective prescriptions based on the electronic medical records data of type 2 diabetes mellitus（T2DM）.

Methods

Based on the real-world electronic medical records data，the database of effective prescriptions of T2DM patients and original prescriptions of different syndromes was constructed. The genetic algorithm was improved and the construction of fitness function was optimized，so the extracted core effective prescriptions can evolve towards the collection of effective prescriptions. Based on the similarity relationship between the core effective prescriptions and the collection of original prescriptions，the recommendation model was innovated，and the prescription recommendation was mined by traversing the collection of original prescriptions of different syndromes.

Results

A total of 17 712 prescriptions with effective diagnosis and treatment standards were used，and 37 kinds of traditional Chinese medicines were extracted. Finally，26 prescriptions with “recommendation value≥85%” were excavated，and the maximal recommendation value was 97.26%.

Conclusion

In this study，the improved genetic algorithm has improved the ability of feature extraction and global search for prescription sets of traditional Chinese medicine，and proposed a new recommendation model for clinical prescriptions decision-making，thus improving the utilization rate of electronic medical records data.

genetic algorithm  /  core prescription  /  prescriptions recommendation  /  real world data  /  type 2 diabetes
刘红萍, 杨杰, 庞国明, 李鹏辉, 邢颖, 吴敏, 温宵宵, 李洪皎, 文天才. 基于改进遗传算法的2型糖尿病中医药有效处方推荐方法研究. 中华中医药学刊, 2025 , 43 (1) : 39 -43 . DOI: 10.13193/j.issn.1673-7717.2025.01.007
Hongping LIU, Jie YANG, Guoming PANG, Penghui LI, Ying XING, Min WU, Xiaoxiao WEN, Hongjiao LI, Tiancai WEN. Study on Effective Prescription Recommendation Method of Traditional Chinese Medicine for Type 2 Diabetes Based on Improved Genetic Algorithm[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (1) : 39 -43 . DOI: 10.13193/j.issn.1673-7717.2025.01.007
Appendix
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Year 2025 volume 43 Issue 1
36
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Article Info
doi: 10.13193/j.issn.1673-7717.2025.01.007
  • Online Date：2026-10-30
  • Published：2025-01-10
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Affiliations
    1.Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
    2.Traditional Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
    3.Kaifeng Traditional Chinese Medicine Hospital, Kaifeng 475000, Henan, China
    4.Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
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https://castjournals.cast.org.cn/joweb/zhzyyxk/EN/10.13193/j.issn.1673-7717.2025.01.007
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