Research on a Risk Prediction Model for Hypoxemia During Spontaneous Breathing Intravenous Anesthesia Using Endoscopic Nasal Masks Based on Machine Learning Algorithms

Abstract

The administration of intravenous anesthesia while maintaining spontaneous breathing presents significant clinical challenges, particularly regarding the maintenance of adequate oxygenation during endoscopic procedures. Hypoxemia remains a primary risk factor in these settings, often arising from respiratory depression or airway obstruction. While the introduction of endoscopic nasal masks has provided a novel interface for simultaneous ventilation and procedural access, the dynamic nature of patient responses necessitates advanced predictive frameworks. This research develops a comprehensive risk prediction model utilizing machine learning algorithms to anticipate hypoxemia events in real-time. By integrating high-dimensional physiological data, procedural variables, and patient-specific metrics, the model identifies non-linear correlations that traditional statistical methods often overlook. The discussion emphasizes the systemic architecture required for deploying such models within clinical workflows, focusing on the trade-offs between algorithmic complexity and interpretability. Furthermore, the paper examines the socio-technical implications of integrating artificial intelligence into perioperative care, addressing issues of algorithmic robustness, clinical governance, and the sustainability of digital health infrastructures. Findings suggest that a multi-agent machine learning approach significantly improves the sensitivity of hypoxemia detection, providing clinicians with a critical window for preemptive intervention. The study concludes with a reflection on the policy frameworks necessary to ensure the fair and safe implementation of predictive modeling in diverse clinical populations, advocating for a human-in-the-loop system design that balances automation with clinical expertise.