Article ; Online: Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach.
Computer methods and programs in biomedicine
2023 Volume 240, Page(s) 107728
Abstract: Background and objective: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic ... ...
Abstract | Background and objective: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model. Methods: A stochastic model was developed using respiratory elastance (E Results: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of E Conclusion: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation. |
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MeSH term(s) | Humans ; Respiration, Artificial/methods ; Retrospective Studies ; Computer Simulation ; Critical Care/methods ; Research Design |
Language | English |
Publishing date | 2023-07-21 |
Publishing country | Ireland |
Document type | Journal Article |
ZDB-ID | 632564-6 |
ISSN | 1872-7565 ; 0169-2607 |
ISSN (online) | 1872-7565 |
ISSN | 0169-2607 |
DOI | 10.1016/j.cmpb.2023.107728 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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