A Home Efficacy Multi-Modal Intelligent Evaluation System for Wearable Treatment Equipment of Insomnia Through Integration Between Traditional Chinese Medicine and Modern Medicine
Bibliographic record
Abstract
Wearable treatment equipment has become a hot topic among traditional Chinese medicine (TCM) researchers. The wearable instrument for transcutaneous electrical stimulation therapy (TEST) developed by our research team has unique advantages in treating insomnia induced by the heart disorder due to phlegm and fire (HDPF). However, the treatment efficacy of the instrument has not been verified by multi-modal data, which limits its application at home. In fact, there is no intelligent efficacy evaluation system for the home treatment of insomnia with the instrument. To make up the gap, this paper attempts to build a home efficacy multi-modal intelligent evaluation system (HEMIES) for this wearable TEST instrument, drawing on the latest technologies of medicine and informatics. Taking HDPF-induced insomnia as an example, the authors firstly set up an index system of the HEMIES for HDPF-induced insomnia, and constructed a conceptual model of the HEMIES. Next, the syndrome images of TCM were fused with the text data of modern medicine, and used to evaluate the treatment efficacy and complete the HEMIES. Finally, 33 patients with HDPF-induced insomnia were recruited to treat the disease with the wearable TEST instrument at home. The observed results show that the prediction accuracy rate of the HEMIES was as high as 90.63%, indicating that the system boasts a good prediction ability and a high value for home application. Our HEMIES realizes the objective evaluation of the home treatment efficacy of the wearable TEST instrument in the absence of TCM practitioners, and provides a reference for the efficacy research of other TCM treatment equipment in home treatment.
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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 | 0.000 |
| 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) | 0.000 | 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".