Recent advances in the study of ancient books on traditional chinese medicine
Bibliographic record
Abstract
The ancient books on traditional Chinese medicine (TCM) are the source of knowledge for TCM physicians. Therapeutic principles and therapeutic methods for healing many diseases are recorded in these ancient TCM books, providing a huge number of references for modern TCM physicians on conducting diagnosis and administering treatment for different diseases. The ancient TCM books can be dated back thousands of years, and this vast knowledge is recorded in different medical books in the form of text. However, it is difficult to systematically assimilate much information in ancient TCM books. At present, many researchers are applying advanced analytical techniques to analyze the text data in the ancient TCM books. Advanced techniques that have been applied include database construction, cognitive linguistic analysis, fuzzy logic, data mining, and artificial intelligence (AI) technology. There are different characteristics in these advanced analytical techniques. In this study, we comprehensively review recent advances in these techniques applied to the study of ancient TCM books. Furthermore, as AI technology is increasingly utilized in the medical field as well as in the study of ancient TCM books, we also review the application of AI technology to the study of ancient TCM books.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".