Evidence map›Paper›PMID 38351686›Full record

ArticleCombinatorial chemistry & high throughput screening2025

Construction and Application of a Traditional Chinese Medicine Syndrome Differentiation Model for Dysmenorrhea Based on Machine Learning.

Limin Zhang, Jianing You, Yiqing Huang, Ruiqi Jing, Yifei He, Yujie Wen, Lulu Zheng, Yong Zhao

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In one paragraph

Article in Combinatorial chemistry & high throughput screening, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

8 authors.

Limin ZhangCollege of Basic Medical, Shanxi University of Chinese Medicine, Taiyuan, Shanxi, China.ORCID 0000-0002-3330-003X
Jianing YouThe Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada.
Yiqing HuangCollege of Basic Medical, Shanxi University of Chinese Medicine, Taiyuan, Shanxi, China.
Ruiqi JingThe Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada.
Yifei HeRotman Commerce, University of Toronto, Toronto, Ontario, Canada.
Yujie WenCollege of Basic Medical, Shanxi University of Chinese Medicine, Taiyuan, Shanxi, China.
Lulu ZhengCollege of Basic Medical, Shanxi University of Chinese Medicine, Taiyuan, Shanxi, China.
Yong ZhaoCollege of Nursing, Shanxi University of Chinese Medicine, Taiyuan, Shanxi, China.

Funding

National Natural Science Foundation of China 81904247Natural Science Foundation of Shanxi Provincial 202103 02122 4297
6 · The paper itself

Abstract

backgroundDysmenorrhea is one of the most common ailments affecting young and middle-aged women, significantly impacting their quality of life. Traditional Chinese Medicine (TCM) offers unique advantages in treating dysmenorrhea. However, an accurate diagnosis is essential to ensure correct treatment. This research integrates the age-old wisdom of TCM with modern Machine Learning (ML) techniques to enhance the precision and efficiency of dysmenorrhea syndrome differentiation, a pivotal process in TCM diagnostics and treatment planning.

methodsA total of 853 effective cases of dysmenorrhea were retrieved from the CNKI database, including patients' syndrome types, symptoms, and features, to establish the TCM information database of dysmenorrhea. Subsequently, 42 critical features were isolated from a potential set of 86 using a selection procedure augmented by Python's Scikit-Learn Library. Various machine learning models were employed, including Logistic Regression, Random Forest Classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), each chosen for their potential to unearth complex patterns within the data.

resultsBased on accuracy, precision, recall, and F1-score metrics, SVM emerged as the most effective model, showcasing an impressive precision of 98.29% and an accuracy of 98.24%. This model's analytical prowess not only highlighted the critical features pivotal to the syndrome differentiation process but also stands to significantly aid clinicians in formulating personalized treatment strategies by pinpointing nuanced symptoms with high precision.

conclusionThe study paves the way for a synergistic approach in TCM diagnostics, merging ancient wisdom with computational acuity, potentially innovating the diagnosis and treatment mode of TCM. Despite the promising outcomes, further research is needed to validate these models in real-world settings and extend this approach to other diseases addressed by TCM.

Indexed as

DysmenorrheaMachine LearningMedicine, Chinese TraditionalFemaleHumansNeural Networks, ComputerSupport Vector MachineDysmenorrhealogistic regressionmachine learningrandom forest classifier.support vector machine (SVM)traditional Chinese medicine diagnostic model

Identifiers

PMID38351686

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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.