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Research on Intelligent Recommendation Method of Ancient Prescription Based on Deep Learning and Medical Case Similarity
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Fengying GUO1, Chenxi GAO1, Yifan WANG1, Pengcheng DU2, Ruojia WANG1, Xing ZHAI1, Tian TIAN2
Chinese Archives of Traditional Chinese Medicine | 2025, 43(6) : 24 - 30
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Chinese Archives of Traditional Chinese Medicine | 2025, 43(6): 24-30
• Digital Traditional Chinese Medicine •
Research on Intelligent Recommendation Method of Ancient Prescription Based on Deep Learning and Medical Case Similarity
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Fengying GUO1, Chenxi GAO1, Yifan WANG1, Pengcheng DU2, Ruojia WANG1, Xing ZHAI1, Tian TIAN2
Affiliations
  • 1.School of Management,Beijing University of Chinese Medicine,Beijing 100105,China
  • 2.School of Traditional Chinese Medicine,Beijing University of Chinese Medicine,Beijing 100105,China
Published: 2025-06-10 doi: 10.13193/j.issn.1673-7717.2025.06.004
Outline
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Objective

To solve the problems of complicated rules of measurement of Chinese medicines in ancient books,inconvenience in finding formulaeand lack of modern intelligent applications for ancient books.

Methods

It summarized the measurement conversion standards and special meter measurement conversion standards in the past dynasties,proposed a similarity calculation method for medical cases based on the fusion of the features of"mainevidence+formulaname+composition of traditional Chinese medicine",and adopted Enhanced Representation from Knowledge IntEgration(ERNIE)model based on deep learning for model training.Itdesigned and developed an intelligent ancient formula recommendation and measurement conversion system based on deep learning recommender engine,using MySQL database and Django framework.Using MySQL database and Django framework,it designed and developed an intelligent ancient prescription recommendation and measurement conversion system based on deep learning recommendation engine.

Results

The overall recommendation accuracy rate obtained by this method reached 89.55%,the precision rate was 94.51%,and the recall rate was 83.97%,which achieved better results,and the system was able to achieve the intelligent retrieval of formulae and the conversion of Chinese medicine measurement units,recommend a series of ancient formulae with a high degree of matching of the main treatment efficacy according to the symptoms inputted by the user,and display the composition of formulas and the Chinese medicine dosage in accordance with the conversion rules of different dynasties.It also displayed the composition of the formula and the dosage ofChinese medicine according to the conversion rules of different dynasties,which conformed to the modern standard measurement.

Conclusion

The method of intelligent recommendation of prescriptions and conversion of Chinese medicine measurements achieves satisfactory results,provides a new way of thinking for the intelligent recommendation of ancient prescriptions,and has certain reference significance for the standardisation of Chinese medicine dosage and the reference significance for the clinical use of medicines.

deep learning  /  similarity of medical records  /  ancient formula  /  intelligent recommendations  /  measurement transformation
郭凤英, 高晨曦, 王一凡, 杜鹏程, 王若佳, 翟兴, 田甜. 基于深度学习和医案相似度的古代方剂智能推荐方法研究. 中华中医药学刊, 2025 , 43 (6) : 24 -30 . DOI: 10.13193/j.issn.1673-7717.2025.06.004
Fengying GUO, Chenxi GAO, Yifan WANG, Pengcheng DU, Ruojia WANG, Xing ZHAI, Tian TIAN. Research on Intelligent Recommendation Method of Ancient Prescription Based on Deep Learning and Medical Case Similarity[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (6) : 24 -30 . DOI: 10.13193/j.issn.1673-7717.2025.06.004
Year 2025 volume 43 Issue 6
28
12
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doi: 10.13193/j.issn.1673-7717.2025.06.004
  • Online Date:2026-10-30
  • Published:2025-06-10
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    1.School of Management,Beijing University of Chinese Medicine,Beijing 100105,China
    2.School of Traditional Chinese Medicine,Beijing University of Chinese Medicine,Beijing 100105,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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