Evidence map›Paper›PMID 41243110›Full record

ArticleChinese medicine2025

TCM-DS: a large language model for intelligent traditional Chinese medicine edible herbal formulas recommendations.

Xuanfeng Li, Haining He, Guibin Lu, Peng Yue, Junying Chen, Zifeng Yang, Chitin Hon

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In one paragraph

Article in Chinese medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xuanfeng Li *Respiratory Disease AI Laboratory in Epidemic Intelligence and Applications of Medical Big Data Instruments, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
Haining He *Respiratory Disease AI Laboratory in Epidemic Intelligence and Applications of Medical Big Data Instruments, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
Guibin LuRespiratory Disease AI Laboratory in Epidemic Intelligence and Applications of Medical Big Data Instruments, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
Peng YueRespiratory Disease AI Laboratory in Epidemic Intelligence and Applications of Medical Big Data Instruments, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
Junying ChenSchool of Software Engineering, South China University of Technology, Guangzhou, Guangdong, China.
Zifeng YangState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Chitin HonRespiratory Disease AI Laboratory in Epidemic Intelligence and Applications of Medical Big Data Instruments, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China. cthon@must.edu.mo.

Funding

National Key Research and Development Program of China 2024YFE0214800Science and Technology Development Fund of Macau SAR FDCT 0002/2024/RDP
6 · The paper itself

Abstract

backgroundThe concept of medicine and food homology in traditional Chinese medicine (TCM) emphasized the dual role of certain material as both food and medicine, offering nutritional and therapeutic benefits. Edible herbal formulas, derived from this principle, are valuable for health management and chronic disease prevention.

methodsThis study proposes a domain-specific prescription recommendation model enriched by TCM edible herbal formula knowledge called TCM-DS model. A dataset including symptoms, TCM constitutions, formulas and their corresponding ingredients was developed. DeepSeek R1 base model was fine-tuned utilizing Low-rank adaptation (LoRA) fine-tuning and a retrieval-augmented generation (RAG) module to increase recommendation accuracy. TCM-DS model was evaluated against general-purpose large language models.

resultsThe proposed TCM-DS model demonstrated superior performance, achieving a recommendation precision of 0.9924. Comparative experiments showed its robustness, with the highest precision scores for both forward and reverse symptom sequences compared with general-purpose large language models. A user-friendly platform was developed based on TCM-DS model, enabling automated constitution analysis and personalized formula recommendations.

conclusionsIn conclusion, we proposed an intelligent TCM edible herbal formula recommendation model called TCM-DS. Its accompanying platform automated constitution identification and formula recommendation, advancing intelligent applications in TCM practice.

Indexed as

Domain adaptationEdible herbal formulaLarge language modelTraditional Chinese medicine

Identifiers

PMID41243110
PMCPMC12621383

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.