HYBRID INTELLIGENT CONTROLLERS FOR A MULTIPLE DRUG DELIVERY SYSTEM IN ACUTE HEART FAILURE
Why this work is in the frame
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Bibliographic record
Abstract
Regulating the dynamic responses to multiple therapeutic agents in cases of heart failure is difficult owing to time-variant changes in drug sensitivity and interaction. To address this problem, a multiple controller based on adaptive neural network (NN) predictive control has been developed for unexpected drug responses related to cardiac output and arterial pressure. However, the control speed may be slower than that in traditional controllers because of the real-time learning process for the NN. Moreover, a proportional-integral-derivative (PID) controller alone cannot automatically update the PID parameters during drug administration. This study, therefore, aimed to make hybrid intelligent (fuzzy or NN-based PID) controllers and to evaluate the control performance during multiple drug therapy in unexpected physiological responses of heart failure. The hybrid intelligent controllers were compared with the previous PID or NN controller, and they realized robust and quick control regardless of unexpected responses and acute disruptions.
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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) | 0.000 | 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) | 0.000 | 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 it