System Identification and Closed-Loop Control of End-Tidal CO<formula formulatype="inline"><tex Notation="TeX">$_{\bf 2}$</tex></formula> in Mechanically Ventilated Patients
Bibliographic record
Abstract
This paper presents a systematic approach to system identification and closed-loop control of end-tidal carbon dioxide partial pressure (PETCO2) in mechanically ventilated patients. An empirical model consisting of a linear dynamic system followed by an affine transform is proposed to derive a low-order and high-fidelity representation that can reproduce the positive and inversely proportional dynamic input-output relationship between PETCO2 and minute ventilation (MV) in mechanically ventilated patients. The predictive capability of the empirical model was evaluated using experimental respiratory data collected from eighteen mechanically ventilated human subjects. The model predicted PETCO2 response accurately with a root-mean-squared error (RMSE) of 0.22+/-0.16 mmHg and a coefficient of determination (r2) of 0.81+/-0.18 (mean+/-SD) when a second-order rational transfer function was used as its linear dynamic component. Using the proposed model, a closedloop control method for PETCO2 based on a proportionalintegral (PI) compensator was proposed by systematic analysis of the system root locus. For the eighteen mechanically ventilated patient models identified, the PI compensator exhibited acceptable closed-loop response with a settling time of 1.27+/- 0.20 min and a negligible overshoot (0.51+/-1.17%), in addition to zero steady-state PETCO2 set point tracking. The physiologic implication of the proposed empirical model was analyzed by comparing it with the traditional multi-compartmental model widely used in pharmacological modeling.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".