Evidence map›Paper›PMID 34804461›Full record

ArticleJournal of healthcare engineering2021

Exploring the Medication Pattern of Chinese Medicine for Peptic Ulcer Based on Data Mining.

Guigui Li, Youlei Guo

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 19% of its field
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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Guigui LiSecond Department of Spleen and Stomach Diseases, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou City 730050, Gansu, China.
Youlei GuoDepartment of Anorectal Diseases, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou City 730050, China.ORCID 0000-0002-6238-6223
Gansu Provincial Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the last decades, Chinese medicine has been widely used for curing various diseases in the healthcare domain. Based on the databases of medicine wisdom and modern application of prescriptions, we have explored the medication pattern of ancient and modern prescriptions for the treatment of peptic ulcer in various patients. In this paper, we have proposed a neural network model which is based on the time series decomposition and is able to mine and predict the medication pattern of peptic ulcer treatment in Chinese medicine. For this purpose, cumulative distance level method, Mann-Kendall trend analysis, Hurst exponent, and characteristic point methods are used for the trend analysis. Likewise in the proposed model, the wavelet analysis method is used for the periodicity analysis and Mann-Kendall mutation test method along with Pettitt methods is used for mutability analysis. In addition, autocorrelation and unit root methods are utilized to test the random terms. The Chinese herbal formulas (where the main diseases are peptic ulcer, peptic ulcer, cerebral leakage, and cerebral abscess) are collected from the databases of medicine wisdom and modern application of prescriptions. Furthermore, methods of frequency analysis, association rule analysis, and factor analysis are used to evaluate the grouping pattern of prescriptions for peptic ulcer treatment. The error in the proposed scheme between the predicted and the measured values of 87 prescriptions, which involve five Chinese medicines for peptic ulcer and 160 Chinese medicines, obtained from the neural network was 16.79%.

Indexed as

Drugs, Chinese HerbalPeptic UlcerData MiningHumansMedicine, Chinese TraditionalDrugs, Chinese Herbal

Identifiers

PMID34804461
PMCPMC8601794
OpenAlexW3214381816

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.