Knowledge Mapping Analysis on Text Mining Research of Medicine Related Fields in Different Regions
Bibliographic record
Abstract
In order to trace the trend of text mining research in medicine related fields through the massive literature, we analyzed the bibliographical reference data of relevant literature in the WOS database with methods of bibliometric and knowledge mapping. We concluded the research state from aspects of time sequence, core authors and institutions, regional and disciplinary distribution; and summarized the research hot points and frontiers through knowledge mapping analysis by using assistant tool CitespaceⅢ. Our analysis indicates that text mining research in medicine related fields appears a steady-state growth trend and state of multidisciplinary integration; and text mining technology has been widely applied to biomedical field such as named entity recognition task, construction and automatic annotation of gene or protein relating corpus, and biomedical event extraction based on various text mining tools. Besides, the research in recent years turns to the EHR information extraction and knowledge discovery, drug knowledge mining and social media mining, etc. In conclusion, it’s worth applying text mining technology to explore medical information, especially clinical information or other aspects more extensively and thoroughly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".