Autors: Karageorgiev I., Ahmed-Shieva, S. A., Shakev, N. G., Minev I.
Title: Conveying Respiratory Care Towards Sub-symbolic Models: Data-Centric Approaches to Mitigate VILI and Patient-Ventilator Asynchrony
Keywords: Computational Intel-ligence, Data Governance, Digital Phenotype, Digital Twins, Machine Learning, Mechanical Ventilation, Respiratory Care

Abstract: Mechanical ventilation is a timeseries rich critical care appliance, it provides insightful bio-signals and metadata about a patient admission. Still invasive respiratory care is a delicate balancing act, for suboptimal control can lead to unfavorable outcomes such as ventilator-induced lung injury (VILI), patient-ventilator asynchrony (PVA) and strain exhaustion. We develop a framework that showcases opportunities for open data acquisitions, and conveys common ventilatory complications to quire solutions via exploratory data analysis and sub-symbolic models. Foremost as an attempt to mitigate and prevent adverse respiratory outcomes, then demonstrate a formalized a data-centric topological opportunities of mechanical ventilators to contribute it's use as a future component of physiological digital twin.

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Issue

International Conference Automatics and Informatics, ICAI 2025 - Proceedings, pp. 283-287, 2026, Bulgaria, https://doi.org/10.1109/ICAI67591.2025.11324843

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus