Ang, Christopher Yew Shuen and Chiew, Yeong Shiong and Wang, Xin and Mat Nor, Mohd Basri and Chase, J. Geoffrey (2023) Stochasticity of the respiratory mechanics during mechanical ventilation treatment. Results in Engineering, 19. ISSN 2590-1230
|
PDF
- Published Version
Download (7MB) | Preview |
|
|
PDF (SCOPUS)
- Supplemental Material
Download (67kB) | Preview |
Abstract
Stochastic models have been used to predict dynamic intra-patient respiratory system elastance (Ers) in mechanically ventilated (MV) patients. However, existing Ers stochastic models were developed using small cohorts, potentially showing bias and overestimation during prediction. Thus, there is a need to improve the stochastic model's performance. This research investigates the effect of the kernel density estimator (KDE) parameter tuned with a constant, c on the performance of a 30-min interval Ers stochastic model. Thirteen variations of a stochastic model were developed using varying KDE parameters. Model bias and overestimation were evaluated by the percentage of actual data captured within the 25th – 75th and 5th – 95th percentile lines (Pass50 and Pass90). The optimum range of c was chosen to tune the KDE parameter and minimise the temporal variations of model-predicted 25th – 75th and 5th – 95th percentile values of Ers (ΔRange50 and ΔRange90) in an independent retrospective clinical cohort of 14 patients. In this cohort, the values of ΔRange50 and ΔRange90 exhibit a converging behaviour, resulting in a cohort-optimised value of c = 0.4. Compared to c = 1.0 (benchmark study model), c = 0.4 significantly reduces model overestimation by up to 25.08% in the 25th – 75th percentile values of Ers. Overall, c = 0.3–1.0 presents as a generalised range of optimum c values, considering the trade-off between data overfitting and model overestimation. Optimisation of the KDE parameter enables more accurate and robust Ers stochastic models in cases of limited training data availability.
| Item Type: | Article (Journal) |
|---|---|
| Additional Information: | 5608/109726 |
| Uncontrolled Keywords: | Stochastic model, Respiratory system elastance, Kernel density estimator, Optimisation |
| Subjects: | R Medicine > RC Internal medicine > RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Medicine Kulliyyah of Medicine > Department of Anaesthesiology & Intensive Care |
| Depositing User: | Dr. Mohd Basri Mat Nor |
| Date Deposited: | 05 Jan 2024 08:48 |
| Last Modified: | 05 Jan 2024 08:48 |
| URI: | http://irep.iium.edu.my/id/eprint/109726 |
Actions (login required)
 |
View Item |
Download Statistics
Download Statistics