Traditional Chinese medical diagnosis based on fuzzy and certainty reasoning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Diagnosis in traditional chinese medicine (TCM) is the foundation of every TCM clinical branch. It is related to basic TCM theories, and guides TCM treatments. In this paper methodologies developed for quick and reliable TCM diagnosis are described. The diagnosis process is divided into two stages. In the first stage the scope of possible diseases and syndromes of a patient will be quickly narrowed down by a modifiable multilevel fuzzy sieving method. This sieving method follows TCM differentiation principles. In the second stage diseases and syndromes will be reliably determined by a multi-agent diagnosis helping system (MADHS) that simulates cooperated experts and makes joint decisions. Fuzziness and uncertainty are incorporated into decision trees to form the reasoning mechanism of agents. A prototype of MADHS has been implemented and successfully tested. It is anticipated that the system and methodologies may be widely used in computer aided diagnosis and TCM education software.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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