AI Ventilator Weaning Timing Prediction System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining the timing of ventilator weaning in critically ill patients lack reliable data, often relying on clinical experience, leading to premature or late weaning, which can result in adverse reactions and prolonged ventilator use.
Innovation Solution
An AI-based machine learning model is developed to predict optimal weaning timing using medical big data, expert experience, and internationally recognized ventilator-weaning parameters, incorporating features like patient age, disease severity, and physiological signs to provide accurate try-weaning and complete-weaning predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clinical experience-based methods are used to determine weaning timing, then decision-making is simple and widely applicable, but prediction accuracy is low and weaning timing is imprecise
Solution Approach 1:
The patent introduces an AI-based prediction system as an intermediary between medical staff and weaning decision-making. The system processes patient data through machine learning models to generate timing predictions, serving as a mediator that enhances decision accuracy without requiring medical staff to perform complex analyses manually.
Solution Approach 2:
The patent replaces the mechanical system of manual clinical assessment with an automated AI-based prediction system. The machine learning model automatically processes patient data, ventilator parameters, and physiological signs to generate weaning timing predictions, substituting human expert judgment with an automated intelligent system.
2Duration of action of moving object
If conservative estimation is used for weaning timing, then safety is improved, but ventilator usage duration is prolonged
Solution Approach 1:
The patent implements a feedback mechanism where the AI system continuously monitors patient responses to weaning attempts and adjusts predictions accordingly. The system learns from actual weaning outcomes to improve future predictions, creating a closed-loop system that balances safety with optimized ventilator usage duration.
Solution Approach 2:
The patent changes the parameters used for weaning decision-making from conservative clinical estimates to data-driven predictions based on multiple patient-specific parameters including age, disease severity, ventilator settings, and physiological signs. This parameter transformation enables more precise timing while maintaining safety through comprehensive data consideration.
3Reliability
If early weaning is attempted to minimize ventilator usage, then ventilator duration is reduced, but re-intubation rate increases
Solution Approach 1:
The patent applies preliminary action by using the AI prediction system to assess patient readiness for weaning before actual weaning attempts are made. The system evaluates multiple parameters in advance to predict the optimal timing, allowing medical staff to prepare appropriately and reduce the risk of re-intubation while minimizing unnecessary ventilator usage.
Data Source
AI summary
A ventilator-weaning timing prediction system, a program product therefor, and methods for building and using the same are disclosed to help a physician to determine a timing for a ventilator-using patient to try to weaning or completely wean from mechanical ventilation using AI-based prediction.