This product utilizes respiratory data derived from ventilator usage at a rate of breaths per second in patients with acute respiratory failure. Through machine learning methods, it can predict in real time the success rate of weaning patients off the ventilator. The predictive model optimizes the clinical decision-making process, leading to tangible benefits in healthcare quality, medical costs, and returns. By leveraging data information to assist clinical decision-making, the process of assessing patients\' readiness to be removed from the ventilator is simplified and optimized, allowing for the identification of the optimal timing for ventilator withdrawal. This improves the duration of ventilator usage, reduces ICU and hospital stays, enhances healthcare quality, and lowers overall medical costs. Additionally, by reducing the time patients spend on ventilators and relieving ICU bed occupancy, it increases the number of patients served and facilitates the efficient utilization of critical care resources.

1. Limited cost, infinite benefits: After implementing the system, the average duration of ventilator usage decreases by 20 hours, and the ventilator weaning rate within 48 hours improves from 96.2% to 98.4%.
2. Alleviating psychological pressure on healthcare professionals: Assisting clinical extubation assessments based on predictive trends and increasing the rate of successful ventilator weaning.
3. Reducing waste and resource consumption: By optimizing the process of assessing patients\' readiness to be removed from the ventilator, unnecessary resource utilization is minimized or reduced.