Design of an Intelligent Symptom Differentiation and Electrical Stimulation Rehabilitation System
Bibliographic record
Abstract
In traditional Chinese medicine (TCM), symptoms are mostly differentiated subjectively by doctors. This approach of symptom differentiation lacks objective basis. Moreover, it is difficult to differentiate between symptoms and treat them through electrical stimulation rehabilitation (ESR) in the absence of TCM doctors. To solve these problems, this paper designs an intelligent symptom differentiation (ISD)-ESR system, which includes a software part for dialectical analysis, and a hardware part for electric stimulation of acupoints. The system was designed with the aid of the following technologies: fuzzy analytic hierarchy process (AHP), chromatographic decomposition, spatiotemporal slicing, optical flow field method, collaborative filtering based on deep neural network (DNN), and software-hardware fusion techniques (e.g. electrical stimulation signal control and Bluetooth multi-pass control). The proposed system was applied to treat 30 patients with primary insomnia in the sleep center of a tertiary hospital. The results show that the proposed system achieved an accuracy of 93.3% in symptom differentiation, and significantly improved the effect of electroacupuncture on insomnia (P<0.05). Overall, the proposed system makes up for the defects of existing devices, and improves the effect of rehabilitation treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".