Syndrome Differentiation System of Traditional Chinese Medicine Based on Bayesian Network
Why this work is in the frame
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Bibliographic record
Abstract
This paper applied Bayesian network to the syndrome differentiation research to improve the quantification of syndrome differentiation diagnosis system in traditional Chinese medicine(TCM).916 syndromes,51 key pattern elements and 1700 syndrome names as nodes collection of Bayesian network were selected for the database,and the framework and probability table for the Bayesian network of TCM syndrome differentiation were made through the network learning.Then,the constructed system was applied to explore the relationship among the syndrome,the key pattern elements and the syndrome names.The computing results were identical to the diagnostic experience of TCM experts,although it still cannot reflect the comprehensiveness of the thinking ability of doctor's syndrome differentiation in TCM.So,the Bayesian network is a good method to deal with the information from the differentiation of symptoms,and can be used to develop the artificial intelligence system for TCM syndrome differentiation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it