ArticleHealthcare informatics research2010
Knowledge structure of korean medical informatics: a social network analysis of articles in journal and proceedings.
Article in Healthcare informatics research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Research trends in Korean medicine based on temporal and network analysis.BMC complementary and alternative medicine · 2019Article
- A Knowledge Map for Hospital Performance Concept: Extraction and Analysis: A Narrative Review Article.Iranian journal of public health · 2016Review
- Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information.Healthcare informatics research · 2014Article
- Application of social network analysis to health care sectors.Healthcare informatics research · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed at exploring the knowledge structure of Korean medical informatics.
methodsWe utilized the keywords, as the main variables, of the research papers that were presented in the journal and symposia of the Korean Society of Medical Informatics, and we used, as cases, the English titles and abstracts of the papers (n = 915) published from 1995 through 2008. N-grams (bigram to 5-gram) were extracted from the corpora using the BiKE Text Analyzer, and their cooccurrence networks were generated via a cosine correlation coefficient, and then the networks were analyzed and visualized using Pajek.
resultsWith the hub and authority measures, the most important research topics in Korean medical informatics were identified. Newly emerging topics by three-year period units were observed as research trends.
conclusionsThis study provides a systematic overview on the knowledge structure of Korean medical informatics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.