Evidence map›Paper›PMID 32481411›Full record

SynthesisMedicine2020

A bibliometric analysis and visualization of medical data mining research.

Yuanzhang Hu, Zeyun Yu, Xiaoen Cheng, Yue Luo, Chuanbiao Wen

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. [One hundred years of theRevista panamericana de salud publica = Pan American journal of public health · 2023
    Article
  19. Article
  20. 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

5 authors.

Yuanzhang HuSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan.
Zeyun YuCollege of Acupuncture and TuiNa, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xiaoen ChengSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan.
Yue LuoSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan.
Chuanbiao WenSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData mining technology used in the field of medicine has been widely studied by scholars all over the world. But there is little research on medical data mining (MDM) from the perspectives of bibliometrics and visualization, and the research topics and development trends in this field are still unclear.

methodsThis paper has applied bibliometric visualization software tools, VOSviewer 1.6.10 and CiteSpace V, to study the citation characteristics, international cooperation, author cooperation, and geographical distribution of the MDM.

resultsA total of 1575 documents are obtained, and the most frequent document type is article (1376). SHAN NH is the most productive author, with the highest number of publications of 12, and the Gillies's article (750 times citation) is the most cited paper. The most productive country and institution in MDM is the USA (559) and US FDA (35), respectively. The Journal of Biomedical Informatics, Expert Systems with Applications and Journal of Medical Systems are the most productive journals, which reflected the nature of the research, and keywords "classification (790)" and "system (576)" have the strongest strength. The hot topics in MDM are drug discovery, medical imaging, vaccine safety, and so on. The 3 frontier topics are reporting system, precision medicine, and inflammation, and would be the foci of future research.

conclusionThe present study provides a panoramic view of data mining methods applied in medicine by visualization and bibliometrics. Analysis of authors, journals, institutions, and countries could provide reference for researchers who are fresh to the field in different ways. Researchers may also consider the emerging trends when deciding the direction of their study.

Indexed as

BibliometricsBiomedical ResearchData MiningHumans

Identifiers

PMID32481411
PMCPMC7748217

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.