Semi-Adaptive Closed-Loop Control for Infusion of Medications With Transport Delay in Clinical Effects
Bibliographic record
Abstract
Abstract This paper presents a semi-adaptive closed-loop control approach to autonomous infusion of medications exhibiting significant transport delay in clinical effects. The basic idea of the approach is to enable stable adaptive control of medication infusion by (1) incorporating transport delay explicitly into control design by way of a Padé approximation while (2) facilitating linear parameterization of control design model by desensitization of nonlinearly parameterized cooperativity constant associated with pharmacodynamics (PD). A novel dynamic dose–response model for control design is presented, in which the cooperativity constant exerts zero influence on the model output in the steady-state. Then, an adaptive pole placement control (APPC) technique was employed to fulfill adaptive control design in the presence of nonminimum phase dynamics associated with the Padé approximation of transport delay. The controller was evaluated in silico using a case study of regulating a cardiovascular variable with a sedative under a wide range of transport delay and pharmacological profiles. The results suggest that adaptation of transport delay and pharmacological characteristics may be beneficial in achieving consistent and robust regulation of medication-elicited clinical effects.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".