A simulation of a medical ventilator with a realistic lungs model
Bibliographic record
Abstract
Background: The outbreak of COVID-19 pandemic highlighted the necessity for accessible and affordable medical ventilators for healthcare providers. To meet this challenge, researchers and engineers world-wide have embarked on an effort to design simple medical ventilators that can be easily distributed. This study provides a simulation model of a simple one-sensor controlled, medical ventilator system including a realistic lungs model and the synchronization between a patient breathing and the ventilator. This model can assist in the design and optimization of these newly developed systems. Methods: The model simulates the ventilator system suggested and built by the “Manshema” team which employs a positive-pressure controlled system, with air and oxygen inputs from a hospital external gas supply. The model was constructed using Simscape™ (MathWorks®) and guidelines for building an equivalent model in OpenModelica software are suggested. The model implements an autonomously breathing, realistic lung model, and was calibrated against the ventilator prototype, accurately simulating the ventilator operation. Results: The model allows studying the expected gas flow and pressure in the patient’s lungs, testing various control schemes and their synchronization with the patient’s breathing. The model components, inputs, and outputs are described, an example for a simple, positive end expiratory pressure control mode is given, and the synchronization with healthy and ARDS patients is analyzed. Conclusions: We provide a simulator of a medical ventilation including realistic, autonomously breathing lungs model. The simulator allows testing different control schemes for the ventilator and its synchronization with a breathing patient. Implementation of this model may assist in efforts to develop simple and accessible medical ventilators to meet the global demand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".