A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical...

Area of Science:

• Medical Technology
• Artificial Intelligence
• Nursing Informatics

Background:

• Improving diagnostic efficiency and reducing missed diagnoses in critical patient care is a significant challenge.
• Current emergency nursing practices can benefit from technological advancements to optimize rescue efforts.
• Automated patient monitoring systems are crucial for timely interventions.

Purpose of the Study:

• To propose a convolutional neural network (CNN) algorithm for optimizing emergency nursing rescue efficiency in critical patients.
• To enhance the ability of pathologists to quickly locate lesion areas and improve diagnostic accuracy.
• To develop a patient behavior recognition system for intelligent medical care.

Main Methods:

• Utilized a CNN algorithm with three convolution layers and varying kernel sizes for feature extraction of patient posture behavior.
• Employed a classifier within the patient posture behavior recognition system to learn feature information and capture nonlinear relationships.
• Applied the algorithm to a patient posture behavior detection system for identification and monitoring.

Main Results:

• The CNN algorithm demonstrated significant improvements in emergency nursing efficiency.
• The patient behavior detection system achieved an average recognition rate of 97.6% for patient posture behavior categories.
• Larger test datasets correlated with higher accuracy in patient posture behavior feature extraction.

Conclusions:

• The developed CNN algorithm effectively improves emergency nursing rescue efficiency for critical patients.
• The patient behavior detection system proves effective and correct, enhancing intelligent medical care levels.
• This AI-driven approach offers a promising solution for optimizing critical patient management and diagnosis.