Coordinated and Semi-Adaptive Cardiorespiratory Control via Sedative and Opioid Drugs
Bibliographic record
Abstract
This paper presents a coordinated semi-adaptive approach to closed-loop control of cardiorespiratory state in critically ill patients through the infusion of sedative and opioid drugs. The proposed approach is built upon an upper level multiple drug coordination loop and a lower level semi-adaptive control loop. The coordination loop recursively adjusts the target set points based on the dose-response relationship of a patient estimated by the semi-adaptive control loop, so as to ensure that the target set points become achievable. The semi-adaptive control loop drives the patient state to the target set points while estimating the patient’s dose-response relationship. Hence, the proposed control approach can adjust target set points erroneously specified by caregivers while respond effectively to the need of individual patients. To realize the proposed control approach, we developed (1) a two-input two-output dose-response model of interacting sedative and opioid drugs; (2) a semi-adaptive control algorithm to drive patient state to target set points while selectively estimating high-sensitivity parameters in the dose-response model; and (3) a multiple drug coordination algorithm that reconciles caregiver preference and individual patient’s drug needs. The proposed control approach was evaluated in an example cardiorespiratory control scenario in which cardiac output and respiratory rate are regulated via the infusion of propofol and remifentanil in an in-silico simulation setting. The results show that the coordinated semi-adaptive control could (1) track achievable target set point with robust transient and steady-state error performance and (2) adjust the unachievable target set points to achievable ones.
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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) | 0.001 | 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 | 0.001 | 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".