Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: With the emerging information and communication technology, the field of medical informatics has dramatically evolved in health care and medicine. Thus, it is crucial to explore the global scientific research landscape on medical informatics. OBJECTIVE: This study aims to present a visual form to clarify the overall scientific research trends of medical informatics in the past decade. METHODS: A bibliometric analysis of data retrieved and extracted from the Web of Science Core Collection (WoSCC) database was performed to analyze global scientific research trends on medical informatics, including publication year, journals, authors, institutions, countries/regions, references, and keywords, from January 1, 2011, to December 31, 2020. RESULTS: The data set recorded 34,742 articles related to medical informatics from WoSCC between 2011 and 2020. The annual global publications increased by 193.86% from 1987 in 2011 to 5839 in 2020. Journal of Medical Internet Research (3600 publications and 63,932 citations) was the most productive and most highly cited journal in the field of medical informatics. David W Bates (99 publications), Harvard University (1161 publications), and the United States (12,927 publications) were the most productive author, institution, and country, respectively. The co-occurrence cluster analysis of high-frequency author keywords formed 4 clusters: (1) artificial intelligence in health care and medicine; (2) mobile health; (3) implementation and evaluation of electronic health records; (4) medical informatics technology application in public health. COVID-19, which ranked third in 2020, was the emerging theme of medical informatics. CONCLUSIONS: We summarize the recent advances in medical informatics in the past decade and shed light on their publication trends, influential journals, global collaboration patterns, basic knowledge, research hotspots, and theme evolution through bibliometric analysis and visualization maps. These findings will accurately and quickly grasp the research trends and provide valuable guidance for future medical informatics research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.026 | 0.130 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".