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[Expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment].
Yongyan Wang1,2,3, Songhua Ma1,2,3, Tianliang Hu1,2,3
1School of Mechanical Engineering, Shandong University, Jinan 250061, P. R. China.
Summary
This study introduces an intelligent system for mechanical ventilation, using fuzzy control and neural networks to adapt settings. It improves patient comfort and provides precise, real-time parameter adjustment suggestions.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Respiratory Care
Context:
- Mechanical ventilation parameter setting requires extensive clinical data and expertise.
- Patient physiological states are dynamic and can change suddenly, complicating parameter adjustments.
- Existing stepwise adjustment methods have limitations in precision and dynamic response.
Purpose:
- To develop an expert knowledge-based strategy for ventilator parameter setting and adaptive adjustment.
- To address challenges in decision-making for time-varying patient states.
- To create a system that provides continuous and smooth mechanical ventilation solutions.
Summary:
- A novel approach combines fuzzy control rules and neural networks for intelligent ventilator parameter management.
- The system analyzes real-time patient physiological data to generate adaptive ventilation strategies.
- It offers explicit, automated parameter adjustment suggestions to healthcare professionals.
Impact:
- Enhances control precision and dynamic quality compared to traditional stepwise adjustments.
- Improves the rationality of multi-input control decision-making in ventilation.
- Significantly increases patient comfort during mechanical ventilation.
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