Bioinformatics, Health Informatics & Medical Informatics: Who searches for what?
Bibliographic record
Abstract
Google Trends is a Google Labs product which provide insights into broad search patterns. In other words, Google Trends allows you to see what the world is searching for. It analyzes Google web searches to compute how many searches have been done for a specific term over the time. It shows the news reference volume & search reference volume graphs as well as the top cities, regions and languages searching a specific term. Using Google Trends, we studied the people's patterns and habits in searching the terms "Bioinformatics", "Health Informatics" & "Medical Informatics". The results show that the users search "Bioinformatics" more than the other two terms. The most searches for "Bioinformatics" were done from India, Kenya, Bangladesh, Singapore & Pakistan. Checking the most frequent languages used for these searches, we determined top 5 languages as Hindi, Korean, Greek, English and Thai which once again confirmed the role of India in this field. Doing the same process for "Health Informatics" as the other keyword, we identified Ireland, United Kingdom, New Zealand, Australia and Canada as the top countries in searching this term; London Colney, Halifax, Dublin, Sydney and London as the top cities; and English, Chinese, Icelandic, Hindi and Czech as the most frequent languages. Considering "Medical Informatics", the most searches have done from Greece, Iran, India, Taiwan and Philippines. The overall results suggest that "Bioinformatics" are more searched than "Health Informatics" and "Medical Informatics". Different countries are interested in different fields of these terms. The dominant search term is "Bioinformatics" and the dominant country in searching this term is India. UK, New Zealand and Australia are more interested in "Health Informatics" while, Iran and Turkey are considerable in searching the term "Medical Informatics". The search patterns of cities may different form the related countries.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".