Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey
Bibliographic record
Abstract
Aim: This study aimed to evaluate the position of Turkey in the field of Medical Informatics and assess the general structure of research by analyzing Medical Informatics research with bibliometric methods. Material and Methods: In this study, we conducted a bibliometric analysis of research and review articles generated between 1980 and 2023 from the Web of Science bibliometric data source, utilizing bibliometric methods through the R bibliometrix tool and VosViewer. Results: In the field of medical informatics research in Turkey, the country holds the 27th position with 905 articles, 15,610 citations, and an impressive impact factor of 51, along with an average citation rate of 17.25 per article, based on bibliometric analysis conducted between 1980 and 2023. Notable institutions in this field include Middle East Technical University, Hacettepe University, and Selçuk University. The prominent research topics encompass "neural network(s), machine learning, support vector, health care, decision support, deep learning, EEG signals, classification accuracy," reflecting the areas of intensive investigation. Conclusion: In Turkey, the field of medical informatics has lagged slightly behind basic engineering sciences or medical sciences. The domain exhibits a multidisciplinary structure intersecting with various engineering fields such as computer science, software engineering, industrial engineering, artificial intelligence engineering, and electronic engineering. To enhance productivity in this field, greater collaboration with other research areas can be pursued. Additionally, it is recommended to urgently establish four-year undergraduate programs specifically dedicated to medical informatics or health informatics at universities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".