Evidence map›Paper›PMID 41606625›Full record

ArticleChinese medicine2026

Cross-domain neural collaborative filtering for personalized herbal prescription recommendation.

Xin Dong, Wansong Zhang, Kuo Yang, Lei Zhang, Runshun Zhang, Juxian Tang, Xinyu Wang, Rouye Huang, Dejiang Ji, Gaxi Ye and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chinese medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

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

11 authors.

Xin Dong *Department of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Wansong Zhang *Department of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Kuo YangDepartment of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China. kuoyang@bjtu.edu.cn.
Lei ZhangNational Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
Runshun ZhangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
Juxian TangDepartment of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Xinyu WangDepartment of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Rouye HuangDepartment of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Dejiang JiDepartment of Acupuncture and Moxibustion, Ningxia Hui Autonomous Region Hospital of TCM, Yinchuan, 750021, China.
Gaxi YeDepartment of Acupuncture and Moxibustion, Ningxia Hui Autonomous Region Hospital of TCM, Yinchuan, 750021, China.
Xuezhong ZhouDepartment of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China. xzzhou@bjtu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 2024YJS032Key Technologies R & D Program of China Academy of Chinese Medical Sciences CI2021A03808National Key Research and Development Program of China 2023YFC3502604National Natural Science Foundation of China 82204941National Natural Science Foundation of China 82374302the Fundamental Research funds for the Central Public Welfare Research institutes 2Z16-XRZ-108-SJthe Key R&D Program Project of Ningxia Hui Autonomous Region 2022BEG02036the Natural Science Foundation of Beijing L232033the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences CI2021A05404
6 · The paper itself

Abstract

objectiveHerbal prescriptions hold significant importance in Traditional Chinese Medicine (TCM) diagnosis and treatment, embodying millennia of clinical case summaries and wisdom. Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling.

methodsIn this study, we introduced a novel herbal prescription recommendation framework with cross-domain neural collaborative filtering (termed PresRecCDL). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, which alleviated the sparsity of data, and the neural collaborative filtering is utilized to carry out prescription recommendations.

resultsComprehensive experiments demonstrate the superiority of the proposed PresRecCDL model over the SOTA model. The effectiveness of each module in PresRecCDL and model robustness are validated by the ablation and hyper-parameter tuning experiments, respectively. The case study based on network pharmacology further validates the effectiveness of the proposed approach, particularly its scientific rigor and feasibility at the molecular mechanism level.

conclusionThis study contributes to enhancing the performance of the HPR model, ultimately benefiting the efficiency and precision of clinical treatment.

Indexed as

Cross-domain learningHerbal prescription recommendationNeural collaborative filtering

Identifiers

PMID41606625
PMCPMC12853632

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LicenceCC BY
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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.