Visualization of the IMIA Yearbook of Medical Informatics Publications over the Last 25 Years
Bibliographic record
Abstract
BACKGROUND: The last 25 years have been a period of innovation in the area of medical informatics. The International Medical Informatics Association (IMIA) has published, every year for the last quarter century, the Yearbook of Medical Informatics, collating selected papers from various journals in an attempt to provide a summary of the academic medical informatics literature. The objective of this paper is to visualize the evolution of the medical informatics field over the last 25 years according to the frequency of word occurrences in the papers published in the IMIA Yearbook of Medical Informatics. METHODS: A literature review was conducted examining the IMIA Yearbook of Medical Informatics between 1992 and 2015. These references were collated into a reference manager application to examine the literature using keyword searches, word clouds, and topic clustering. The data was considered in its entirety, as well as segregated into 3 time periods to examine the evolution of main trends over time. Several methods were used, including word clouds, cluster maps, and custom developed web-based information dashboards. RESULTS: The literature search resulted in a total of 1210 references published in the Yearbook, of which 213 references were excluded, resulting in 997 references for visualization. Overall, we found that publications were more technical and methods-oriented between 1992 and 1999; more clinically and patient-oriented between 2000 and 2009; and noted the emergence of "big data", decision support, and global health in the past decade between 2010 and 2015. Dashboards were additionally created to show individual reference data, as well as, aggregated information. CONCLUSION: Medical informatics is a vast and expanding area with new methods and technologies being researched, implemented, and evaluated. Determining visualization approaches that enhance our understanding of literature is an active area of research, and like medical informatics, is constantly evolving as new software and algorithms are developed. This paper examined several approaches for visualizing the medical informatics literature to show historical trends, associations, and aggregated summarized information to illustrate the state and changes in the IMIA Yearbook publications over the last quarter century.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.129 | 0.159 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".