Evidence map›Paper›PMID 36142090›Full record

ArticleInternational journal of environmental research and public health2022

Machine Learning Applications for the Development of a Questionnaire to Identify Sasang Constitution Typology.

Soon Mi Kim, Jeongkun Ryu, Eunhye Olivia Park

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Soon Mi KimDepartment of Food and Nutrition, College of BioNano Technology, Gachon University, Seongnam 13120, Korea.
Jeongkun RyuDepartment of Food and Nutrition, College of BioNano Technology, Gachon University, Seongnam 13120, Korea.
Eunhye Olivia ParkDepartment of Food and Nutrition, College of BioNano Technology, Gachon University, Seongnam 13120, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sasang constitutional medicine emphasizes personalized disease prevention and treatment and has been used in various fields. Nevertheless, more efforts are required to improve the validity and reliability of the Sasang analysis tools. Hence, this study aimed to (1) identify key constructs and measurement items of the Sasang constitution questionnaire that characterize different Sasang constitutions and (2) investigate the similarities and differences in pathophysiological and personality traits between Sasang constitutions. The results of the Sasang constitution questionnaire were analyzed using multiple machine learning-based approaches, including feature selection, hierarchical clustering analysis, and multiple correspondence analysis. The selected 47 key measurement items were clustered into six groups based on the similarity measures. The findings of this study are expected to be beneficial for future research on the development of more robust and reliable Sasang conservation questionnaires, allowing Sasang constitutional medicine to be more widely implemented in various sectors.

Indexed as

Machine LearningMedicine, Korean TraditionalReproducibility of ResultsSurveys and Questionnairesfeature selectionhierarchical clustering analysismachine learningSasang constitutional medicineSasang constitution typology

Identifiers

PMID36142090
PMCPMC9517306

What OpenQuestion holds

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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.