Evidence map›Paper›PMID 37920309›Full record

ArticleTanaffos2023

Exploring Common Symptoms in Patients with Respiratory Allergies Using K-Means Algorithm in the North-East of Iran in 2012-2015.

Somaye Norouzi, Samane Sistani, Maryam Khoshkhui, Reza Faridhosseini, Payam Payandeh, Fahimeh Ghasemian, Leila Ahmadian, Mohammadhossein Pourasad, Farahzad Jabbari Azad

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In one paragraph

Article in Tanaffos, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 60% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 4 institutions in 1 country.

Somaye NorouziStudent Research Committee, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran.
Samane SistaniDepartment of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Maryam KhoshkhuiAllergy Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Reza FaridhosseiniAllergy Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Payam PayandehDepartment of Pediatrics, School of Medicine, Non-Communicable Pediatric Disease Research Center, Health Research Institute, Amirkola Hospital, Babol University of Medical Sciences, Babol, Iran.
Fahimeh GhasemianDepartment of Computer Engineering, Faculty of Engineering, Shahid Bahonar University, Kerman, Iran.
Leila AhmadianDepartment of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran.
Mohammadhossein PourasadDepartment of Health Information Technology, School of Paramedical, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Farahzad Jabbari AzadAllergy Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Mashhad University of Medical Sciences · IRKerman University of Medical Sciences · IRKermanshah University of Medical Sciences · IRShahid Bahonar University of Kerman · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As a common disease among people of almost any age, allergic rhinitis has many adverse effects such as lowering the quality of life and efficiency at work or school. Considering these conditions and the collection of large amounts of data, the present research was conducted on allergic rhinitis and asthma patients' data to extract the common symptoms of these diseases using cluster analysis and the k-means algorithm. Materials and Methods: The present cross-sectional research was conducted in Mashhad city. The inclusion criteria were affliction with one or two respiratory allergy diseases diagnosed by an allergy specialist through clinical history taking and physical examination. A researcher-made checklist was used in the present study for data collection. Then, the K-means algorithm's cluster analysis model was conducted to extract clusters (WEKA software (3, 6, 9)). Results: Overall, 1,231 patients met the inclusion criteria. The result of the Cluster analysis consisted of Cluster 1 in allergic rhinitis consisted of 702 patients, and cluster 2 consisted of 382 patients.46 asthma patients were assigned to cluster 1 and 23 to cluster 2.Also, 60 asthma and allergic rhinitis patients were assigned to cluster 1 and 19 to cluster 2. The most common symptoms in all patients were rhinorrhea, sneezing, nasal congestion, and itchy nose. Conclusion: Overall,

Indexed as

Allergic rhinitisAsthmaCluster analysisData mining

Identifiers

PMID37920309
PMCPMC10618594
OpenAlexW4388302494

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LicenceCC BY-NC
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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.